psychological research on working memory

REVIEW article

Working memory from the psychological and neurosciences perspectives: a review.

• 1 Department of Neurosciences, School of Medical Sciences, Universiti Sains Malaysia, Kubang Kerian, Malaysia
• 2 Center for Neuroscience Services and Research, Universiti Sains Malaysia, Kubang Kerian, Malaysia

Since the concept of working memory was introduced over 50 years ago, different schools of thought have offered different definitions for working memory based on the various cognitive domains that it encompasses. The general consensus regarding working memory supports the idea that working memory is extensively involved in goal-directed behaviors in which information must be retained and manipulated to ensure successful task execution. Before the emergence of other competing models, the concept of working memory was described by the multicomponent working memory model proposed by Baddeley and Hitch. In the present article, the authors provide an overview of several working memory-relevant studies in order to harmonize the findings of working memory from the neurosciences and psychological standpoints, especially after citing evidence from past studies of healthy, aging, diseased, and/or lesioned brains. In particular, the theoretical framework behind working memory, in which the related domains that are considered to play a part in different frameworks (such as memory’s capacity limit and temporary storage) are presented and discussed. From the neuroscience perspective, it has been established that working memory activates the fronto-parietal brain regions, including the prefrontal, cingulate, and parietal cortices. Recent studies have subsequently implicated the roles of subcortical regions (such as the midbrain and cerebellum) in working memory. Aging also appears to have modulatory effects on working memory; age interactions with emotion, caffeine and hormones appear to affect working memory performances at the neurobiological level. Moreover, working memory deficits are apparent in older individuals, who are susceptible to cognitive deterioration. Another younger population with working memory impairment consists of those with mental, developmental, and/or neurological disorders such as major depressive disorder and others. A less coherent and organized neural pattern has been consistently reported in these disadvantaged groups. Working memory of patients with traumatic brain injury was similarly affected and shown to have unusual neural activity (hyper- or hypoactivation) as a general observation. Decoding the underlying neural mechanisms of working memory helps support the current theoretical understandings concerning working memory, and at the same time provides insights into rehabilitation programs that target working memory impairments from neurophysiological or psychological aspects.

Introduction

Working memory has fascinated scholars since its inception in the 1960’s ( Baddeley, 2010 ; D’Esposito and Postle, 2015 ). Indeed, more than a century of scientific studies revolving around memory in the fields of psychology, biology, or neuroscience have not completely agreed upon a unified categorization of memory, especially in terms of its functions and mechanisms ( Cowan, 2005 , 2008 ; Baddeley, 2010 ). From the coining of the term “memory” in the 1880’s by Hermann Ebbinghaus, to the distinction made between primary and secondary memory by William James in 1890, and to the now widely accepted and used categorizations of memory that include: short-term, long-term, and working memories, studies that have tried to decode and understand this abstract concept called memory have been extensive ( Cowan, 2005 , 2008 ). Short and long-term memory suggest that the difference between the two lies in the period that the encoded information is retained. Other than that, long-term memory has been unanimously understood as a huge reserve of knowledge about past events, and its existence in a functioning human being is without dispute ( Cowan, 2008 ). Further categorizations of long-term memory include several categories: (1) episodic; (2) semantic; (3) Pavlovian; and (4) procedural memory ( Humphreys et al., 1989 ). For example, understanding and using language in reading and writing demonstrates long-term storage of semantics. Meanwhile, short-term memory was defined as temporarily accessible information that has a limited storage time ( Cowan, 2008 ). Holding a string of meaningless numbers in the mind for brief delays reflects this short-term component of memory. Thus, the concept of working memory that shares similarities with short-term memory but attempts to address the oversimplification of short-term memory by introducing the role of information manipulation has emerged ( Baddeley, 2012 ). This article seeks to present an up-to-date introductory overview of the realm of working memory by outlining several working memory studies from the psychological and neurosciences perspectives in an effort to refine and unite the scientific knowledge concerning working memory.

The Multicomponent Working Memory Model

When one describes working memory, the multicomponent working memory model is undeniably one of the most prominent working memory models that is widely cited in literatures ( Baars and Franklin, 2003 ; Cowan, 2005 ; Chein et al., 2011 ; Ashkenazi et al., 2013 ; D’Esposito and Postle, 2015 ; Kim et al., 2015 ). Baddeley and Hitch (1974) proposed a working memory model that revolutionized the rigid and dichotomous view of memory as being short or long-term, although the term “working memory” was first introduced by Miller et al. (1960) . The working memory model posited that as opposed to the simplistic functions of short-term memory in providing short-term storage of information, working memory is a multicomponent system that manipulates information storage for greater and more complex cognitive utility ( Baddeley and Hitch, 1974 ; Baddeley, 1996 , 2000b ). The three subcomponents involved are phonological loop (or the verbal working memory), visuospatial sketchpad (the visual-spatial working memory), and the central executive which involves the attentional control system ( Baddeley and Hitch, 1974 ; Baddeley, 2000b ). It was not until 2000 that another component termed “episodic buffer” was introduced into this working memory model ( Baddeley, 2000a ). Episodic buffer was regarded as a temporary storage system that modulates and integrates different sensory information ( Baddeley, 2000a ). In short, the central executive functions as the “control center” that oversees manipulation, recall, and processing of information (non-verbal or verbal) for meaningful functions such as decision-making, problem-solving or even manuscript writing. In Baddeley and Hitch (1974) ’s well-cited paper, information received during the engagement of working memory can also be transferred to long-term storage. Instead of seeing working memory as merely an extension and a useful version of short-term memory, it appears to be more closely related to activated long-term memory, as suggested by Cowan (2005 , 2008 ), who emphasized the role of attention in working memory; his conjectures were later supported by Baddeley (2010) . Following this, the current development of the multicomponent working memory model could be retrieved from Baddeley’s article titled “Working Memory” published in Current Biology , in Figure 2 ( Baddeley, 2010 ).

An Embedded-Processes Model of Working Memory

Notwithstanding the widespread use of the multicomponent working memory model, Cowan (1999 , 2005 ) proposed the embedded-processes model that highlights the roles of long-term memory and attention in facilitating working memory functioning. Arguing that the Baddeley and Hitch (1974) model simplified perceptual processing of information presentation to the working memory store without considering the focus of attention to the stimuli presented, Cowan (2005 , 2010 ) stressed the pivotal and central roles of working memory capacity for understanding the working memory concept. According to Cowan (2008) , working memory can be conceptualized as a short-term storage component with a capacity limit that is heavily dependent on attention and other central executive processes that make use of stored information or that interact with long-term memory. The relationships between short-term, long-term, and working memory could be presented in a hierarchical manner whereby in the domain of long-term memory, there exists an intermediate subset of activated long-term memory (also the short-term storage component) and working memory belongs to the subset of activated long-term memory that is being attended to ( Cowan, 1999 , 2008 ). An illustration of Cowan’s theoretical framework on working memory can be traced back to Figure 1 in his paper titled “What are the differences between long-term, short-term, and working memory?” published in Progress in Brain Research ( Cowan, 2008 ).

Alternative Models

Cowan’s theoretical framework toward working memory is consistent with Engle (2002) ’s view, in which it was posited that working memory capacity is comparable to directed or held attention information inhibition. Indeed, in their classic study on reading span and reading comprehension, Daneman and Carpenter (1980) demonstrated that working memory capacity, which was believed to be reflected by the reading span task, strongly correlated with various comprehension tests. Surely, recent and continual growth in the memory field has also demonstrated the development of other models such as the time-based resource-sharing model proposed by several researchers ( Barrouillet et al., 2004 , 2009 ; Barrouillet and Camos, 2007 ). This model similarly demonstrated that cognitive load and working memory capacity that were so often discussed by working memory researchers were mainly a product of attention that one receives to allocate to tasks at hand ( Barrouillet et al., 2004 , 2009 ; Barrouillet and Camos, 2007 ). In fact, the allocated cognitive resources for a task (such as provided attention) and the duration of such allocation dictated the likelihood of success in performing the tasks ( Barrouillet et al., 2004 , 2009 ; Barrouillet and Camos, 2007 ). This further highlighted the significance of working memory in comparison with short-term memory in that, although information retained during working memory is not as long-lasting as long-term memory, it is not the same and deviates from short-term memory for it involves higher-order processing and executive cognitive controls that are not observed in short-term memory. A more detailed presentation of other relevant working memory models that shared similar foundations with Cowan’s and emphasized the roles of long-term memory can be found in the review article by ( D’Esposito and Postle, 2015 ).

In addition, in order to understand and compare similarities and disparities in different proposed models, about 20 years ago, Miyake and Shah (1999) suggested theoretical questions to authors of different models in their book on working memory models. The answers to these questions and presentations of models by these authors gave rise to a comprehensive definition of working memory proposed by Miyake and Shah (1999 , p. 450), “working memory is those mechanisms or processes that are involved in the control, regulation, and active maintenance of task-relevant information in the service of complex cognition, including novel as well as familiar, skilled tasks. It consists of a set of processes and mechanisms and is not a fixed ‘place’ or ‘box’ in the cognitive architecture. It is not a completely unitary system in the sense that it involves multiple representational codes and/or different subsystems. Its capacity limits reflect multiple factors and may even be an emergent property of the multiple processes and mechanisms involved. Working memory is closely linked to LTM, and its contents consist primarily of currently activated LTM representations, but can also extend to LTM representations that are closely linked to activated retrieval cues and, hence, can be quickly activated.” That said, in spite of the variability and differences that have been observed following the rapid expansion of working memory understanding and its range of models since the inception of the multicomponent working memory model, it is worth highlighting that the roles of executive processes involved in working memory are indisputable, irrespective of whether different components exist. Such notion is well-supported as Miyake and Shah, at the time of documenting the volume back in the 1990’s, similarly noted that the mechanisms of executive control were being heavily investigated and emphasized ( Miyake and Shah, 1999 ). In particular, several domains of working memory such as the focus of attention ( Cowan, 1999 , 2008 ), inhibitory controls ( Engle and Kane, 2004 ), maintenance, manipulation, and updating of information ( Baddeley, 2000a , 2010 ), capacity limits ( Cowan, 2005 ), and episodic buffer ( Baddeley, 2000a ) were executive processes that relied on executive control efficacy (see also Miyake and Shah, 1999 ; Barrouillet et al., 2004 ; D’Esposito and Postle, 2015 ).

The Neuroscience Perspective

Following such cognitive conceptualization of working memory developed more than four decades ago, numerous studies have intended to tackle this fascinating working memory using various means such as decoding its existence at the neuronal level and/or proposing different theoretical models in terms of neuronal activity or brain activation patterns. Table 1 offers the summarized findings of these literatures. From the cognitive neuroscientific standpoint, for example, the verbal and visual-spatial working memories were examined separately, and the distinction between the two forms was documented through studies of patients with overt impairment in short-term storage for different verbal or visual tasks ( Baddeley, 2000b ). Based on these findings, associations or dissociations with the different systems of working memory (such as phonological loops and visuospatial sketchpad) were then made ( Baddeley, 2000b ). It has been established that verbal and acoustic information activates Broca’s and Wernicke’s areas while visuospatial information is represented in the right hemisphere ( Baddeley, 2000b ). Not surprisingly, many supporting research studies have pointed to the fronto-parietal network involving the dorsolateral prefrontal cortex (DLPFC), the anterior cingulate cortex (ACC), and the parietal cortex (PAR) as the working memory neural network ( Osaka et al., 2003 ; Owen et al., 2005 ; Chein et al., 2011 ; Kim et al., 2015 ). More precisely, the DLPFC has been largely implicated in tasks demanding executive control such as those requiring integration of information for decision-making ( Kim et al., 2015 ; Jimura et al., 2017 ), maintenance and manipulation/retrieval of stored information or relating to taxing loads (such as capacity limit) ( Osaka et al., 2003 ; Moore et al., 2013 ; Vartanian et al., 2013 ; Rodriguez Merzagora et al., 2014 ), and information updating ( Murty et al., 2011 ). Meanwhile, the ACC has been shown to act as an “attention controller” that evaluates the needs for adjustment and adaptation of received information based on task demands ( Osaka et al., 2003 ), and the PAR has been regarded as the “workspace” for sensory or perceptual processing ( Owen et al., 2005 ; Andersen and Cui, 2009 ). Figure 1 attempted to translate the theoretical formulation of the multicomponent working memory model ( Baddeley, 2010 ) to specific regions in the human brain. It is, however, to be acknowledged that the current neuroscientific understanding on working memory adopted that working memory, like other cognitive systems, involves the functional integration of the brain as a whole; and to clearly delineate its roles into multiple components with only a few regions serving as specific buffers was deemed impractical ( D’Esposito and Postle, 2015 ). Nonetheless, depicting the multicomponent working memory model in the brain offers a glimpse into the functional segregation of working memory.

TABLE 1. Working memory (WM) studies in the healthy brain.

FIGURE 1. A simplified depiction (adapted from the multicomponent working memory model by Baddeley, 2010 ) as implicated in the brain, in which the central executive assumes the role to exert control and oversee the manipulation of incoming information for intended execution. ACC, Anterior cingulate cortex.

Further investigation has recently revealed that other than the generally informed cortical structures involved in verbal working memory, basal ganglia, which lies in the subcortical layer, plays a role too ( Moore et al., 2013 ). Particularly, the caudate and thalamus were activated during task encoding, and the medial thalamus during the maintenance phase, while recorded activity in the fronto-parietal network, which includes the DLPFC and the parietal lobules, was observed only during retrieval ( Moore et al., 2013 ). These findings support the notion that the basal ganglia functions to enhance focusing on a target while at the same time suppressing irrelevant distractors during verbal working memory tasks, which is especially crucial at the encoding phase ( Moore et al., 2013 ). Besides, a study conducted on mice yielded a similar conclusion in which the mediodorsal thalamus aided the medial prefrontal cortex in the maintenance of working memory ( Bolkan et al., 2017 ). In another study by Murty et al. (2011) in which information updating, which is one of the important aspects of working memory, was investigated, the midbrain including the substantia nigra/ventral tegmental area and caudate was activated together with DLPFC and other parietal regions. Taken together, these studies indicated that brain activation of working memory are not only limited to the cortical layer ( Murty et al., 2011 ; Moore et al., 2013 ). In fact, studies on cerebellar lesions subsequently discovered that patients suffered from impairments in attention-related working memory or executive functions, suggesting that in spite of the motor functions widely attributed to the cerebellum, the cerebellum is also involved in higher-order cognitive functions including working memory ( Gottwald et al., 2004 ; Ziemus et al., 2007 ).

Shifting the attention to the neuronal network involved in working memory, effective connectivity analysis during engagement of a working memory task reinforced the idea that the DLPFC, PAR and ACC belong to the working memory circuitry, and bidirectional endogenous connections between all these regions were observed in which the left and right PAR were the modeled input regions ( Dima et al., 2014 ) (refer to Supplementary Figure 1 in Dima et al., 2014 ). Effective connectivity describes the attempt to model causal influence of neuronal connections in order to better understand the hidden neuronal states underlying detected neuronal responses ( Friston et al., 2013 ). Another similar study of working memory using an effective connectivity analysis that involved more brain regions, including the bilateral middle frontal gyrus (MFG), ACC, inferior frontal cortex (IFC), and posterior parietal cortex (PPC) established the modulatory effect of working memory load in this fronto-parietal network with memory delay as the driving input to the bilateral PPC ( Ma et al., 2012 ) (refer to Figure 1 in Ma et al., 2012 ).

Moving away from brain regions activated but toward the in-depth neurobiological side of working memory, it has long been understood that the limited capacity of working memory and its transient nature, which are considered two of the defining characteristics of working memory, indicate the role of persistent neuronal firing (see Review Article by D’Esposito and Postle, 2015 ; Zylberberg and Strowbridge, 2017 ; see also Silvanto, 2017 ), that is, continuous action potentials are generated in neurons along the neural network. However, this view was challenged when activity-silent synaptic mechanisms were found to also be involved ( Mongillo et al., 2008 ; Rose et al., 2016 ; see also Silvanto, 2017 ). Instead of holding relevant information through heightened and persistent neuronal firing, residual calcium at the presynaptic terminals was suggested to have mediated the working memory process ( Mongillo et al., 2008 ). This synaptic theory was further supported when TMS application produced a reactivation effect of past information that was not needed or attended at the conscious level, hence the TMS application facilitated working memory efficacy ( Rose et al., 2016 ). As it happens, this provided evidence from the neurobiological viewpoint to support Cowan’s theorized idea of “activated long-term memory” being a feature of working memory as non-cued past items in working memory that were assumed to be no longer accessible were actually stored in a latent state and could be brought back into consciousness. However, the researchers cautioned the use of the term “activated long-term memory” and opted for “prioritized long-term memory” because these unattended items maintained in working memory seemed to employ a different mechanism than items that were dropped from working memory ( Rose et al., 2016 ). Other than the synaptic theory, the spiking working memory model proposed by Fiebig and Lansner (2017) that borrowed the concept from fast Hebbian plasticity similarly disagreed with persistent neuronal activity and demonstrated that working memory processes were instead manifested in discrete oscillatory bursts.

Age and Working Memory

Nevertheless, having established a clear working memory circuitry in the brain, differences in brain activations, neural patterns or working memory performances are still apparent in different study groups, especially in those with diseased or aging brains. For a start, it is well understood that working memory declines with age ( Hedden and Gabrieli, 2004 ; Ziaei et al., 2017 ). Hence, older participants are expected to perform poorer on a working memory task when making comparison with relatively younger task takers. In fact, it was reported that decreases in cortical surface area in the frontal lobe of the right hemisphere was associated with poorer performers ( Nissim et al., 2017 ). In their study, healthy (those without mild cognitive impairments [MCI] or neurodegenerative diseases such as dementia or Alzheimer’s) elderly people with an average age of 70 took the n-back working memory task while magnetic resonance imaging (MRI) scans were obtained from them ( Nissim et al., 2017 ). The outcomes exhibited that a decrease in cortical surface areas in the superior frontal gyrus, pars opercularis of the inferior frontal gyrus, and medial orbital frontal gyrus that was lateralized to the right hemisphere, was significantly detected among low performers, implying an association between loss of brain structural integrity and working memory performance ( Nissim et al., 2017 ). There was no observed significant decline in cortical thickness of the studied brains, which is assumed to implicate neurodegenerative tissue loss ( Nissim et al., 2017 ).

Moreover, another extensive study that examined cognitive functions of participants across the lifespan using functional magnetic resonance imaging (fMRI) reported that the right lateralized fronto-parietal regions in addition to the ventromedial prefrontal cortex (VMPFC), posterior cingulate cortex, and left angular and middle frontal gyri (the default mode regions) in older adults showed reduced modulation of task difficulty, which was reflective of poorer task performance ( Rieck et al., 2017 ). In particular, older-age adults (55–69 years) exhibited diminished brain activations (positive modulation) as compared to middle-age adults (35–54 years) with increasing task difficulty, whereas lesser deactivation (negative modulation) was observed between the transition from younger adults (20–34 years) to middle-age adults ( Rieck et al., 2017 ). This provided insights on cognitive function differences during an individual’s lifespan at the neurobiological level, which hinted at the reduced ability or efficacy of the brain to modulate functional regions to increased difficulty as one grows old ( Rieck et al., 2017 ). As a matter of fact, such an opinion was in line with the Compensation-Related Utilization of Neural Circuits Hypothesis (CRUNCH) proposed by Reuter-Lorenz and Cappell (2008) . The CRUNCH likewise agreed upon reduced neural efficiency in older adults and contended that age-associated cognitive decline brought over-activation as a compensatory mechanism; yet, a shift would occur as task loads increase and under-activation would then be expected because older adults with relatively lesser cognitive resources would max out their ‘cognitive reserve’ sooner than younger adults ( Reuter-Lorenz and Park, 2010 ; Schneider-Garces et al., 2010 ).

In addition to those findings, emotional distractors presented during a working memory task were shown to alter or affect task performance in older adults ( Oren et al., 2017 ; Ziaei et al., 2017 ). Based on the study by Oren et al. (2017) who utilized the n-back task paired with emotional distractors with neutral or negative valence in the background, negative distractors with low load (such as 1-back) resulted in shorter response time (RT) in the older participants ( M age = 71.8), although their responses were not significantly more accurate when neutral distractors were shown. Also, lesser activations in the bilateral MFG, VMPFC, and left PAR were reported in the old-age group during negative low load condition. This finding subsequently demonstrated the results of emotional effects on working memory performance in older adults ( Oren et al., 2017 ). Further functional connectivity analyses revealed that the amygdala, the region well-known to be involved in emotional processing, was deactivated and displayed similar strength in functional connectivity regardless of emotional or load conditions in the old-age group ( Oren et al., 2017 ). This finding went in the opposite direction of that observed in the younger group in which the amygdala was strongly activated with less functional connections to the bilateral MFG and left PAR ( Oren et al., 2017 ). This might explain the shorter reported RT, which was an indication of improved working memory performance, during the emotional working memory task in the older adults as their amygdala activation was suppressed as compared to the younger adults ( Oren et al., 2017 ).

Interestingly, a contrasting neural connection outcome was reported in the study by Ziaei et al. (2017) in which differential functional networks relating to emotional working memory task were employed by the two studied groups: (1) younger ( M age = 22.6) and (2) older ( M age = 68.2) adults. In the study, emotional distractors with positive, neutral, and negative valence were presented during a visual working memory task and older adults were reported to adopt two distinct networks involving the VMPFC to encode and process positive and negative distractors while younger adults engaged only one neural pathway ( Ziaei et al., 2017 ). The role of amygdala engagement in processing only negative items in the younger adults, but both negative and positive distractors in the older adults, could be reflective of the older adults’ better ability at regulating negative emotions which might subsequently provide a better platform for monitoring working memory performance and efficacy as compared to their younger counterparts ( Ziaei et al., 2017 ). This study’s findings contradict those by Oren et al. (2017) in which the amygdala was found to play a bigger role in emotional working memory tasks among older participants as opposed to being suppressed as reported by Oren et al. (2017) . Nonetheless, after overlooking the underlying neural mechanism relating to emotional distractors, it was still agreed that effective emotional processing sustained working memory performance among older/elderly people ( Oren et al., 2017 ; Ziaei et al., 2017 ).

Aside from the interaction effect between emotion and aging on working memory, the impact of caffeine was also investigated among elders susceptible to age-related cognitive decline; and those reporting subtle cognitive deterioration 18-months after baseline measurement showed less marked effects of caffeine in the right hemisphere, unlike those with either intact cognitive ability or MCI ( Haller et al., 2017 ). It was concluded that while caffeine’s effects were more pronounced in MCI participants, elders in the early stages of cognitive decline displayed diminished sensitivity to caffeine after being tested with the n-back task during fMRI acquisition ( Haller et al., 2017 ). It is, however, to be noted that the working memory performance of those displaying minimal cognitive deterioration was maintained even though their brain imaging uncovered weaker brain activation in a more restricted area ( Haller et al., 2017 ). Of great interest, such results might present a useful brain-based marker that can be used to identify possible age-related cognitive decline.

Similar findings that demonstrated more pronounced effects of caffeine on elderly participants were reported in an older study, whereas older participants in the age range of 50–65 years old exhibited better working memory performance that offset the cognitive decline observed in those with no caffeine consumption, in addition to displaying shorter reaction times and better motor speeds than observed in those without caffeine ( Rees et al., 1999 ). Animal studies using mice showed replication of these results in mutated mice models of Alzheimer’s disease or older albino mice, both possibly due to the reported results of reduced amyloid production or brain-derived neurotrophic factor and tyrosine-kinase receptor. These mice performed significantly better after caffeine treatment in tasks that supposedly tapped into working memory or cognitive functions ( Arendash et al., 2006 ). Such direct effects of caffeine on working memory in relation to age was further supported by neuroimaging studies ( Haller et al., 2013 ; Klaassen et al., 2013 ). fMRI uncovered increased brain activation in regions or networks of working memory, including the fronto-parietal network or the prefrontal cortex in old-aged ( Haller et al., 2013 ) or middle-aged adults ( Klaassen et al., 2013 ), even though the behavioral measures of working memory did not differ. Taken together, these outcomes offered insight at the neurobiological level in which caffeine acts as a psychoactive agent that introduces changes and alters the aging brain’s biological environment that explicit behavioral testing might fail to capture due to performance maintenance ( Haller et al., 2013 , 2017 ; Klaassen et al., 2013 ).

With respect to physiological effects on cognitive functions (such as effects of caffeine on brain physiology), estradiol, the primary female sex hormone that regulates menstrual cycles, was found to also modulate working memory by engaging different brain activity patterns during different phases of the menstrual cycle ( Joseph et al., 2012 ). The late follicular (LF) phase of the menstrual cycle, characterized by high estradiol levels, was shown to recruit more of the right hemisphere that was associated with improved working memory performance than did the early follicular (EF) phase, which has lower estradiol levels although overall, the direct association between estradiol levels and working memory was inconclusive ( Joseph et al., 2012 ). The finding that estradiol levels modified brain recruitment patterns at the neurobiological level, which could indirectly affect working memory performance, presents implications that working memory impairment reported in post-menopausal women (older aged women) could indicate a link with estradiol loss ( Joseph et al., 2012 ). In 2000, post-menopausal women undergoing hormone replacement therapy, specifically estrogen, were found to have better working memory performance in comparison with women who took estrogen and progestin or women who did not receive the therapy ( Duff and Hampson, 2000 ). Yet, interestingly, a study by Janowsky et al. (2000) showed that testosterone supplementation counteracted age-related working memory decline in older males, but a similar effect was not detected in older females who were supplemented with estrogen. A relatively recent paper might have provided the explanation to such contradicting outcomes ( Schöning et al., 2007 ). As demonstrated in the study using fMRI, the nature of the task (such as verbal or visual-spatial) might have played a role as a higher level of testosterone (in males) correlated with activations of the left inferior parietal cortex, which was deemed a key region in spatial processing that subsequently brought on better performance in a mental-rotation task. In contrast, significant correlation between estradiol and other cortical activations in females in the midluteal phase, who had higher estradiol levels, did not result in better performance of the task compared to women in the EF phase or men ( Schöning et al., 2007 ). Nonetheless, it remains premature to conclude that age-related cognitive decline was a result of hormonal (estradiol or testosterone) fluctuations although hormones might have modulated the effect of aging on working memory.

Other than the presented interaction effects of age and emotions, caffeine, and hormones, other studies looked at working memory training in the older population in order to investigate working memory malleability in the aging brain. Findings of improved performance for the same working memory task after training were consistent across studies ( Dahlin et al., 2008 ; Borella et al., 2017 ; Guye and von Bastian, 2017 ; Heinzel et al., 2017 ). Such positive results demonstrated effective training gains regardless of age difference that could even be maintained until 18 months later ( Dahlin et al., 2008 ) even though the transfer effects of such training to other working memory tasks need to be further elucidated as strong evidence of transfer with medium to large effect size is lacking ( Dahlin et al., 2008 ; Guye and von Bastian, 2017 ; Heinzel et al., 2017 ; see also Karbach and Verhaeghen, 2014 ). The studies showcasing the effectiveness of working memory training presented a useful cognitive intervention that could partially stall or delay cognitive decline. Table 2 presents an overview of the age-related working memory studies.

TABLE 2. Working memory (WM) studies in relation to age.

The Diseased Brain and Working Memory

Age is not the only factor influencing working memory. In recent studies, working memory deficits in populations with mental or neurological disorders were also being investigated (see Table 3 ). Having identified that the working memory circuitry involves the fronto-parietal region, especially the prefrontal and parietal cortices, in a healthy functioning brain, targeting these areas in order to understand how working memory is affected in a diseased brain might provide an explanation for the underlying deficits observed at the behavioral level. For example, it was found that individuals with generalized or social anxiety disorder exhibited reduced DLPFC activation that translated to poorer n-back task performance in terms of accuracy and RT when compared with the controls ( Balderston et al., 2017 ). Also, VMPFC and ACC, representing the default mode network (DMN), were less inhibited in these individuals, indicating that cognitive resources might have been divided and resulted in working memory deficits due to the failure to disengage attention from persistent anxiety-related thoughts ( Balderston et al., 2017 ). Similar speculation can be made about individuals with schizophrenia. Observed working memory deficits might be traced back to impairments in the neural networks that govern attentional-control and information manipulation and maintenance ( Grot et al., 2017 ). The participants performed a working memory binding task, whereby they had to make sure that the word-ellipse pairs presented during the retrieval phase were identical to those in the encoding phase in terms of location and verbal information; results concluded that participants with schizophrenia had an overall poorer performance compared to healthy controls when they were asked to actively bind verbal and spatial information ( Grot et al., 2017 ). This was reflected in the diminished activation in the schizophrenia group’s ventrolateral prefrontal cortex and the PPC that were said to play a role in manipulation and reorganization of information during encoding and maintenance of information after encoding ( Grot et al., 2017 ).

TABLE 3. Working memory (WM) studies in the diseased brain.

In addition, patients with major depressive disorder (MDD) displayed weaker performance in the working memory updating domain in which information manipulation was needed when completing a visual working memory task ( Le et al., 2017 ). The working memory task employed in the study was a delayed recognition task that required participants to remember and recognize the faces or scenes as informed after stimuli presentation while undergoing fMRI scan ( Le et al., 2017 ). Subsequent functional connectivity analyses revealed that the fusiform face area (FFA), parahippocampal place area (PPA), and left MFG showed aberrant activity in the MDD group as compared to the control group ( Le et al., 2017 ). These brain regions are known to be the visual association area and the control center of working memory and have been implicated in visual working memory updating in healthy adults ( Le et al., 2017 ). Therefore, altered visual cortical functions and load-related activation in the prefrontal cortex in the MDD group implied that the cognitive control for visual information processing and updating might be impaired at the input or control level, which could have ultimately played a part in the depressive symptoms ( Le et al., 2017 ).

Similarly, during a verbal delayed match to sample task that asked participants to sub-articulatorly rehearse presented target letters for subsequent letter-matching, individuals with bipolar affective disorder displayed aberrant neural interactions between the right amygdala, which is part of the limbic system implicated in emotional processing as previously described, and ipsilateral cortical regions often concerned with verbal working memory, pointing out that the cortico-amygdalar connectivity was disrupted, which led to verbal working memory deficits ( Stegmayer et al., 2015 ). As an attempt to gather insights into previously reported hyperactivation in the amygdala in bipolar affective disorder during an articulatory working memory task, functional connectivity analyses revealed that negative functional interactions seen in healthy controls were not replicated in patients with bipolar affective disorder ( Stegmayer et al., 2015 ). Consistent with the previously described study about emotional processing effects on working memory in older adults, this reported outcome was suggestive of the brain’s failed attempts to suppress pathological amygdalar activation during a verbal working memory task ( Stegmayer et al., 2015 ).

Another affected group with working memory deficits that has been the subject of research interest was children with developmental disorders such as attention deficit/hyperactivity disorder (ADHD), developmental dyscalculia, and reading difficulties ( Rotzer et al., 2009 ; Ashkenazi et al., 2013 ; Wang and Gathercole, 2013 ; Maehler and Schuchardt, 2016 ). For instance, looking into the different working memory subsystems based on Baddeley’s multicomponent working memory model in children with dyslexia and/or ADHD and children with dyscalculia and/or ADHD through a series of tests, it was reported that distinctive working memory deficits by groups could be detected such that phonological loop (e.g., digit span) impairment was observed in the dyslexia group, visuospatial sketchpad (e.g., Corsi block tasks) deficits in the dyscalculia group, while central executive (e.g., complex counting span) deficits in children with ADHD ( Maehler and Schuchardt, 2016 ). Meanwhile, examination of working memory impairment in a delayed match-to-sample visual task that put emphasis on the maintenance phase of working memory by examining the brainwaves of adults with ADHD using electroencephalography (EEG) also revealed a marginally significantly lower alpha band power in the posterior regions as compared to healthy individuals, and such an observation was not significantly improved after working memory training (Cogmed working memory training, CWMT Program) ( Liu et al., 2016 ). The alpha power was considered important in the maintenance of working memory items; and lower working memory accuracy paired with lower alpha band power was indeed observed in the ADHD group ( Liu et al., 2016 ).

Not dismissing the above compiled results, children encountering disabilities in mathematical operations likewise indicated deficits in the working memory domain that were traceable to unusual brain activities at the neurobiological level ( Rotzer et al., 2009 ; Ashkenazi et al., 2013 ). It was speculated that visuospatial working memory plays a vital role when arithmetic problem-solving is involved in order to ensure intact mental representations of the numerical information ( Rotzer et al., 2009 ). Indeed, Ashkenazi et al. (2013) revealed that Block Recall, a variant of the Corsi Block Tapping test and a subtest of the Working Memory Test Battery for Children (WMTB-C) that explored visuospatial sketchpad ability, was significantly predictive of math abilities. In relation to this, studies investigating brain activation patterns and performance of visuospatial working memory task in children with mathematical disabilities identified the intraparietal sulcus (IPS), in conjunction with other regions in the prefrontal and parietal cortices, to have less activation when visuospatial working memory was deemed involved (during an adapted form of Corsi Block Tapping test made suitable for fMRI [ Rotzer et al., 2009 ]); in contrast the control group demonstrated correlations of the IPS in addition to the fronto-parietal cortical activation with the task ( Rotzer et al., 2009 ; Ashkenazi et al., 2013 ). These brain activity variations that translated to differences in overt performances between healthily developing individuals and those with atypical development highlighted the need for intervention and attention for the disadvantaged groups.

Traumatic Brain Injury and Working Memory

Physical injuries impacting the frontal or parietal lobes would reasonably be damaging to one’s working memory. This is supported in studies employing neuropsychological testing to assess cognitive impairments in patients with traumatic brain injury; and poorer cognitive performances especially involving the working memory domains were reported (see Review Articles by Dikmen et al., 2009 ; Dunning et al., 2016 ; Phillips et al., 2017 ). Research on cognitive deficits in traumatic brain injury has been extensive due to the debilitating conditions brought upon an individual daily life after the injury. Traumatic brain injuries (TBI) refer to accidental damage to the brain after being hit by an object or following rapid acceleration or deceleration ( Farrer, 2017 ). These accidents include falls, assaults, or automobile accidents and patients with TBI can be then categorized into three groups; (1) mild TBI with GCS – Glasgow Coma Scale – score of 13–15; (2) moderate TBI with GCS score of 9–12; and (3) severe TBI with GCS score of 3–8 ( Farrer, 2017 ). In a recently published meta-analysis that specifically looked at working memory impairments in patients with moderate to severe TBI, patients displayed reduced cognitive functions in verbal short-term memory in addition to verbal and visuospatial working memory in comparison to control groups ( Dunning et al., 2016 ). It was also understood from the analysis that the time lapse since injury and age of injury were deciding factors that influenced these cognitive deficits in which longer time post-injury or older age during injury were associated with greater cognitive decline ( Dunning et al., 2016 ).

Nonetheless, it is to be noted that such findings relating to age of injury could not be generalized to the child population since results from the pediatric TBI cases showed that damage could negatively impact developmental skills that could indicate a greater lag in cognitive competency as the child’s frontal lobe had yet to mature ( Anderson and Catroppa, 2007 ; Mandalis et al., 2007 ; Nadebaum et al., 2007 ; Gorman et al., 2012 ). These studies all reported working memory impairment of different domains such as attentional control, executive functions, or verbal and visuospatial working memory in the TBI group, especially for children with severe TBI ( Mandalis et al., 2007 ; Nadebaum et al., 2007 ; Gorman et al., 2012 ). Investigation of whether working memory deficits are domain-specific or -general or involve one or more mechanisms, has yielded inconsistent results. For example, Perlstein et al. (2004) found that working memory was impaired in the TBI group only when complex manipulation such as sequential coding of information is required and not accounted for by processing speed or maintenance of information, but two teams of researchers ( Perbal et al., 2003 ; Gorman et al., 2012 ) suggested otherwise. From their study on timing judgments, Perbal et al. (2003) concluded that deficits were not related to time estimation but more on generalized attentional control, working memory and processing speed problems; while Gorman et al. (2012) also attributed the lack of attentional focus to impairments observed during the working memory task. In fact, in a later study by Gorman et al. (2016) , it was shown that processing speed mediated TBI effects on working memory even though the mediation was partial. On the other hand, Vallat-Azouvi et al. (2007) reported impairments in the working memory updating domain that came with high executive demands for TBI patients. Also, Mandalis et al. (2007) similarly highlighted potential problems with attention and taxing cognitive demands in the TBI group.

From the neuroscientific perspective, hyper-activation or -connectivity in the working memory circuitry was reported in TBI patients in comparison with healthy controls when both groups engaged in working memory tasks, suggesting that the brain attempted to compensate for or re-establish lost connections upon the injury ( Dobryakova et al., 2015 ; Hsu et al., 2015 ; Wylie et al., 2015 ). For a start, it was observed that participants with mild TBI displayed increased activation in the right prefrontal cortex during a working memory task when comparing to controls ( Wylie et al., 2015 ). Interestingly, this activation pattern only occurred in patients who did not experience a complete recovery 1 week after the injury ( Wylie et al., 2015 ). Besides, low activation in the DMN was observed in mild TBI patients without cognitive recovery, and such results seemed to be useful in predicting recovery in patients in which the patients did not recover when hypoactivation (low activation) was reported, and vice versa ( Wylie et al., 2015 ). This might be suggestive of the potential of cognitive recovery simply by looking at the intensity of brain activation of the DMN, for an increase in activation of the DMN seemed to be superseded before cognitive recovery was present ( Wylie et al., 2015 ).

In fact, several studies lent support to the speculation mentioned above as hyperactivation or hypoactivation in comparison with healthy participants was similarly identified. When sex differences were being examined in working memory functional activity in mild TBI patients, hyperactivation was reported in male patients when comparing to the male control group, suggesting that the hyperactivation pattern might be the brain’s attempt at recovering impaired functions; even though hypoactivation was shown in female patients as compared to the female control group ( Hsu et al., 2015 ). The researchers from the study further explained that such hyperactivation after the trauma acted as a neural compensatory mechanism so that task performance could be maintained while hypoactivation with a poorer performance could have been the result of a more severe injury ( Hsu et al., 2015 ). Therefore, the decrease in activation in female patients, in addition to the observed worse performance, was speculated to be due to a more serious injury sustained by the female patients group ( Hsu et al., 2015 ).

In addition, investigation of the effective connectivity of moderate and severe TBI participants during a working memory task revealed that the VMPFC influenced the ACC in these TBI participants when the opposite was observed in healthy subjects ( Dobryakova et al., 2015 ). Moreover, increased inter-hemispheric transfer due to an increased number of connections between the left and right hemispheres (hyper-connectivity) without clear directionality of information flow (redundant connectivity) was also reported in the TBI participants ( Dobryakova et al., 2015 ). This study was suggestive of location-specific changes in the neural network connectivity following TBI depending on the cognitive functions at work, other than providing another support to the neural compensatory hypothesis due to the observed hyper-connectivity ( Dobryakova et al., 2015 ).

Nevertheless, inconsistent findings should not be neglected. In a study that also focused on brain connectivity analysis among patients with mild TBI by Hillary et al. (2011) , elevated task-related connectivity in the right hemisphere, in particular the prefrontal cortex, was consistently demonstrated during a working memory task while the control group showed greater left hemispheric activation. This further supported the right lateralization of the brain to reallocate cognitive resources of TBI patients post-injury. Meanwhile, the study did not manage to obtain the expected outcome in terms of greater clustering of whole-brain connections in TBI participants as hypothesized ( Hillary et al., 2011 ). That said, no significant loss or gain of connections due to the injury could be concluded from the study, as opposed to the hyper- or hypoactivation or hyper-connectivity frequently highlighted in other similar researches ( Hillary et al., 2011 ). Furthermore, a study by Chen et al. (2012) also failed to establish the same results of increased brain activation. Instead, with every increase of the working memory load, increase in brain activation, as expected to occur and as demonstrated in the control group, was unable to be detected in the TBI group ( Chen et al., 2012 ).

Taken all the insightful studies together, another aspect not to be neglected is the neuroimaging techniques employed in contributing to the literature on TBI. Modalities other than fMRI, which focuses on localization of brain activities, show other sides of the story of working memory impairments in TBI to offer a more holistic understanding. Studies adopting electroencephalography (EEG) or diffusor tensor imaging (DTI) reported atypical brainwaves coherence or white matter integrity in patients with TBI ( Treble et al., 2013 ; Ellis et al., 2016 ; Bailey et al., 2017 ; Owens et al., 2017 ). Investigating the supero-lateral medial forebrain bundle (MFB) that innervates and consequently terminates at the prefrontal cortex, microstructural white matter damage at the said area was indicated in participants with moderate to severe TBI by comparing its integrity with the control group ( Owens et al., 2017 ). Such observation was backed up by evidence showing that the patients performed more poorly on attention-loaded cognitive tasks of factors relating to slow processing speed than the healthy participants, although a direct association between MFB and impaired attentional system was not found ( Owens et al., 2017 ).

Correspondingly, DTI study of the corpus callosum (CC), which described to hold a vital role in connecting and coordinating both hemispheres to ensure competent cognitive functions, also found compromised microstructure of the CC with low fractional anisotropy and high mean diffusivity, both of which are indications of reduced white matter integrity ( Treble et al., 2013 ). This reported observation was also found to be predictive of poorer verbal or visuospatial working memory performance in callosal subregions connecting the parietal and temporal cortices ( Treble et al., 2013 ). Adding on to these results, using EEG to examine the functional consequences of CC damage revealed that interhemispheric transfer time (IHTT) of the CC was slower in the TBI group than the control group, suggesting an inefficient communication between the two hemispheres ( Ellis et al., 2016 ). In addition, the TBI group with slow IHTT as well exhibited poorer neurocognitive functioning including working memory than the healthy controls ( Ellis et al., 2016 ).

Furthermore, comparing the working memory between TBI, MDD, TBI-MDD, and healthy participants discovered that groups with MDD and TBI-MDD performed poorer on the Sternberg working memory task but functional connectivity on the other hand, showed that increased inter-hemispheric working memory gamma connectivity was observed in the TBI and TBI-MDD groups ( Bailey et al., 2017 ). Speculation provided for the findings of such neuronal state that was not reflected in the explicit working memory performance was that the deficits might not be detected or tested by the utilized Sternberg task ( Bailey et al., 2017 ). Another explanation attempting to answer the increase in gamma connectivity in these groups was the involvement of the neural compensatory mechanism after TBI to improve performance ( Bailey et al., 2017 ). Nevertheless, such outcome implies that behavioral performances or neuropsychological outcomes might not always be reflective of the functional changes happening in the brain.

Yet, bearing in mind that TBI consequences can be vast and crippling, cognitive improvement or recovery, though complicated due to the injury severity-dependent nature, is not impossible (see Review Article by Anderson and Catroppa, 2007 ; Nadebaum et al., 2007 ; Dikmen et al., 2009 ; Chen et al., 2012 ). As reported by Wylie et al. (2015) , cognitive improvement together with functional changes in the brain could be detected in individuals with mild TBI. Increased activation in the brain during 6-week follow-up was also observed in the mild TBI participants, implicating the regaining of connections in the brain ( Chen et al., 2012 ). Administration of certain cognitively enhancing drugs such as methylphenidate was reported to be helpful in improving working memory performance too ( Manktelow et al., 2017 ). Methylphenidate as a dopamine reuptake inhibitor was found to have modulated the neural activity in the left cerebellum which subsequently correlated with improved working memory performance ( Manktelow et al., 2017 ). A simplified summary of recent studies on working memory and TBI is tabulated in Table 4 .

TABLE 4. Working memory (WM) studies in the TBI group.

General Discussion and Future Direction

In practice, all of the aforementioned studies contribute to the working memory puzzle by addressing the topic from different perspectives and employing various methodologies to study it. Several theoretical models of working memory that conceptualized different working memory mechanisms or domains (such as focus of attention, inhibitory controls, maintenance and manipulation of information, updating and integration of information, capacity limits, evaluative and executive controls, and episodic buffer) have been proposed. Coupled with the working memory tasks of various means that cover a broad range (such as Sternberg task, n-back task, Corsi block-tapping test, Wechsler’s Memory Scale [WMS], and working memory subtests in the Wechsler Adult Intelligence Scale [WAIS] – Digit Span, Letter Number Sequencing), it has been difficult, if not highly improbable, for working memory studies to reach an agreement upon a consistent study protocol that is acceptable for generalization of results due to the constraints bound by the nature of the study. Various data acquisition and neuroimaging techniques that come with inconsistent validity such as paper-and-pen neuropsychological measures, fMRI, EEG, DTI, and functional near-infrared spectroscopy (fNIRS), or even animal studies can also be added to the list. This poses further challenges to quantitatively measure working memory as only a single entity. For example, when studying the neural patterns of working memory based on Cowan’s processes-embedded model using fMRI, one has to ensure that the working memory task selected is fMRI-compatible, and demands executive control of attention directed at activated long-term memory (domain-specific). That said, on the one hand, there are tasks that rely heavily on the information maintenance such as the Sternberg task; on the other hand, there are also tasks that look into the information manipulation updating such as the n-back or arithmetic task. Meanwhile, the digit span task in WAIS investigates working memory capacity, although it can be argued that it also encompasses the domain on information maintenance and updating-. Another consideration involves the different natures (verbal/phonological and visuospatial) of the working memory tasks as verbal or visuospatial information is believed to engage differing sensory mechanisms that might influence comparison of working memory performance between tasks of different nature ( Baddeley and Hitch, 1974 ; Cowan, 1999 ). For instance, though both are n-back tasks that includes the same working memory domains, the auditory n-back differs than the visual n-back as the information is presented in different forms. This feature is especially crucial with regards to the study populations as it differentiates between verbal and visuospatial working memory competence within individuals, which are assumed to be domain-specific as demonstrated by vast studies (such as Nadler and Archibald, 2014 ; Pham and Hasson, 2014 ; Nakagawa et al., 2016 ). These test variations undeniably present further difficulties in selecting an appropriate task. Nevertheless, the adoption of different modalities yielded diverging outcomes and knowledge such as behavioral performances, functional segregation and integration in the brain, white matter integrity, brainwave coherence, and oxy- and deoxyhaemoglobin concentrations that are undeniably useful in application to different fields of study.

In theory, the neural efficiency hypothesis explains that increased efficiency of the neural processes recruit fewer cerebral resources in addition to displaying lower activation in the involved neural network ( Vartanian et al., 2013 ; Rodriguez Merzagora et al., 2014 ). This is in contrast with the neural compensatory hypothesis in which it attempted to understand diminished activation that is generally reported in participants with TBI ( Hillary et al., 2011 ; Dobryakova et al., 2015 ; Hsu et al., 2015 ; Wylie et al., 2015 ; Bailey et al., 2017 ). In the diseased brain, low activation has often been associated with impaired cognitive function ( Chen et al., 2012 ; Dobryakova et al., 2015 ; Wylie et al., 2015 ). Opportunely, the CRUNCH model proposed within the field of aging might be translated and integrated the two hypotheses here as it suitably resolved the disparity of cerebral hypo- and hyper-activation observed in weaker, less efficient brains as compared to healthy, adept brains ( Reuter-Lorenz and Park, 2010 ; Schneider-Garces et al., 2010 ). Moreover, other factors such as the relationship between fluid intelligence and working memory might complicate the current understanding of working memory as a single, isolated construct since working memory is often implied in measurements of the intelligence quotient ( Cowan, 2008 ; Vartanian et al., 2013 ). Indeed, the process overlap theory of intelligence proposed by Kovacs and Conway (2016) in which the constructs of intelligence were heavily scrutinized (such as general intelligence factors, g and its smaller counterparts, fluid intelligence or reasoning, crystallized intelligence, perceptual speed, and visual-spatial ability), and fittingly connected working memory capacity with fluid reasoning. Cognitive tests such as Raven’s Progressive Matrices or other similar intelligence tests that demand complex cognition and were reported in the paper had been found to correlate strongly with tests of working memory ( Kovacs and Conway, 2016 ). Furthermore, in accordance with such views, in the same paper, neuroimaging studies found intelligence tests also activated the same fronto-parietal network observed in working memory ( Kovacs and Conway, 2016 ).

On the other hand, even though the roles of the prefrontal cortex in working memory have been widely established, region specificity and localization in the prefrontal cortex in relation to the different working memory domains such as manipulation or delayed retention of information remain at the premature stage (see Review Article by D’Esposito and Postle, 2015 ). It has been postulated that the neural mechanisms involved in working memory are of high-dimensionality and could not always be directly captured and investigated using neurophysiological techniques such as fMRI, EEG, or patch clamp recordings even when comparing with lesion data ( D’Esposito and Postle, 2015 ). According to D’Esposito and Postle (2015) , human fMRI studies have demonstrated that a rostral-caudal functional gradient related to level of abstraction required of working memory along the frontal cortex (in which different regions in the prefrontal cortex [from rostral to caudal] might be associated with different abstraction levels) might exist. Other functional gradients relating to different aspects of working memory were similarly unraveled ( D’Esposito and Postle, 2015 ). These proposed mechanisms with different empirical evidence point to the fact that conclusive understanding regarding working memory could not yet be achieved before the inconsistent views are reconciled.

Not surprisingly, with so many aspects of working memory yet to be understood and its growing complexity, the cognitive neuroscience basis of working memory requires constant research before an exhaustive account can be gathered. From the psychological conceptualization of working memory as attempted in the multicomponent working memory model ( Baddeley and Hitch, 1974 ), to the neural representations of working memory in the brain, especially in the frontal regions ( D’Esposito and Postle, 2015 ), one important implication derives from the present review of the literatures is that working memory as a psychological construct or a neuroscientific mechanism cannot be investigated as an isolated event. The need for psychology and neuroscience to interact with each other in an active feedback cycle exists in which this cognitive system called working memory can be dissected at the biological level and refined both empirically, and theoretically.

In summary, the present article offers an account of working memory from the psychological and neuroscientific perspectives, in which theoretical models of working memory are presented, and neural patterns and brain regions engaging in working memory are discussed among healthy and diseased brains. It is believed that working memory lays the foundation for many other cognitive controls in humans, and decoding the working memory mechanisms would be the first step in facilitating understanding toward other aspects of human cognition such as perceptual or emotional processing. Subsequently, the interactions between working memory and other cognitive systems could reasonably be examined.

Author Contributions

WC wrote the manuscript with critical feedback and consultation from AAH. WC and AAH contributed to the final version of the manuscript. JA supervised the process and proofread the manuscript.

This work was supported by the Transdisciplinary Research Grant Scheme (TRGS) 203/CNEURO/6768003 and the USAINS Research Grant 2016.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

The reviewer EB and handling Editor declared their shared affiliation.

Andersen, R. A., and Cui, H. (2009). Review intention, action planning, and decision making in parietal-frontal circuits. Neuron 63, 568–583. doi: 10.1016/j.neuron.2009.08.028

PubMed Abstract | CrossRef Full Text | Google Scholar

Anderson, V., and Catroppa, C. (2007). Memory outcome at 5 years post-childhood traumatic brain injury. Brain Inj. 21, 1399–1409. doi: 10.1080/02699050701785070

Arendash, G. W., Schleif, W., Rezai-Zadeh, K., Jackson, E. K., Zacharia, L. C., Cracchiolo, J. R., et al. (2006). Caffeine protects Alzheimer’s mice against cognitive impairment and reduces brain β-amyloid production. Neuroscience 142, 941–952. doi: 10.1016/j.neuroscience.2006.07.021

Ashkenazi, S., Rosenberg-lee, M., Metcalfe, A. W. S., Swigart, A. G., and Menon, V. (2013). Neuropsychologia visuo – spatial working memory is an important source of domain-general vulnerability in the development of arithmetic cognition. Neuropsychologia 51, 2305–2317. doi: 10.1016/j.neuropsychologia.2013.06.031

Baars, B. J., and Franklin, S. (2003). How conscious experience and working memory interact. Trends Cogn. Sci. 7, 166–172. doi: 10.1016/S1364-6613(03)00056-1

CrossRef Full Text | Google Scholar

Baddeley, A. (1996). Exploring the central executive. Q. J. Exp. Psychol. A 49, 5–28. doi: 10.1080/713755608

Baddeley, A. (2010). Working memory. Curr. Biol. 20, R136–R140. doi: 10.1016/j.cub.2009.12.014

Baddeley, A. (2012). Working memory: theories, models, and controversies. Annu. Rev. Psychol. 63, 1–29. doi: 10.1146/annurev-psych-120710-100422

Baddeley, A., and Hitch, G. (1974). Working memory. Psychol. Learn. Motiv. 8, 47–89. doi: 10.1016/j.cub.2009.12.014

Baddeley, A. D. (2000a). The episodic buffer : a new component of working memory? Trends Cogn. Sci. 4, 417–423. doi: 10.1016/S1364-6613(00)01538-2

Baddeley, A. D. (2000b). Short-Term and Working Memory. The Oxford Handbook of Memory. Oxford: Oxford University Press.

Google Scholar

Bailey, N. W., Rogasch, N. C., Hoy, K. E., Maller, J. J., Segrave, R. A., Sullivan, C. M., et al. (2017). Increased gamma connectivity during working memory retention following traumatic brain injury. Brain Inj. 31, 379–389. doi: 10.1080/02699052.2016.1239273

Balderston, N. L., Vytal, K. E., O’Connell, K., Torrisi, S., Letkiewicz, A., Ernst, M., et al. (2017). Anxiety patients show reduced working memory related dlPFC activation during safety and threat. Depress. Anxiety 34, 25–36. doi: 10.1002/da.22518

Barrouillet, P., Bernardin, S., and Camos, V. (2004). Time constraints and resource sharing in adults’ working memory spans. J. Exp. Psychol. Gen. 133, 83–100. doi: 10.1037/0096-3445.133.1.83

Barrouillet, P., and Camos, V. (2007). “The time-based resource-sharing model of working memory,” in The Cognitive Neuroscience of Working Memory , ed. N. Osaka (Oxford: Oxford University Press), 59–80. doi: 10.1093/acprof:oso/9780198570394.003.0004

Barrouillet, P., Gavens, N., Vergauwe, E., Gaillard, V., and Camos, V. (2009). Working memory span development: a time-based resource-sharing model account. Dev. Psychol. 45, 477–490. doi: 10.1037/a0014615

Bolkan, S. S., Stujenske, J. M., Parnaudeau, S., Spellman, T. J., Rauffenbart, C., Abbas, A. I., et al. (2017). Thalamic projections sustain prefrontal activity during working memory maintenance. Nat. Neurosci. 20, 987–996. doi: 10.1038/nn.4568

Borella, E., Carretti, B., Sciore, R., Capotosto, E., Taconnat, L., Cornoldi, C., et al. (2017). Training working memory in older adults: is there an advantage of using strategies? Psychol. Aging 32, 178–191. doi: 10.1037/pag0000155

Chein, J. M., Moore, A. B., and Conway, A. R. A. (2011). NeuroImage domain-general mechanisms of complex working memory span. Neuroimage 54, 550–559. doi: 10.1016/j.neuroimage.2010.07.067

Chen, C. J., Wu, C. H., Liao, Y. P., Hsu, H. L., Tseng, Y. C., Liu, H. L., et al. (2012). Working memory in patients with mild traumatic brain injury: functional MR imaging analysis. Radiology 264, 844–851. doi: 10.1148/radiol.12112154

Cowan, N. (1999). “An embedded-processes model of working memory,” in Models of Working Memory: Mechanisms of Active Maintenance and Executive Control , eds A. Miyake and P. Shah (Cambridge: Cambridge University Press). doi: 10.1017/S0140525X01003922

Cowan, N. (2005). Working memory capacity. Exp. Psychol. 54, 245–246. doi: 10.1027/1618-3169.54.3.245

Cowan, N. (2008). What are the differences between long-term, short-term, and working memory? Prog. Brain Res. 169, 323–338. doi: 10.1016/S0079-6123(07)00020-9

Cowan, N. (2010). The magical mystery four. Curr. Dir. Psychol. Sci. 19, 51–57. doi: 10.1177/0963721409359277

Dahlin, E., Nyberg, L., Bäckman, L., and Neely, A. S. (2008). Plasticity of executive functioning in young and older adults: immediate training gains, transfer, and long-term maintenance. Psychol. Aging 23, 720–730. doi: 10.1037/a0014296

Daneman, M., and Carpenter, P. A. (1980). Individual differences in working memory and reading. J. Verbal Learn. Verbal Behav. 19, 450–466. doi: 10.1016/S0022-5371(80)90312-6

D’Esposito, M., and Postle, B. R. (2015). The cognitive neuroscience of working memory. Annu. Rev. Psychol. 66, 115–142. doi: 10.1146/annurev-psych-010814-015031

Dikmen, S. S., Corrigan, J. D., Levin, H. S., Machamer, J., Stiers, W., and Weisskopf, M. G. (2009). Cognitive outcome following traumatic brain injury. J. Head Trauma Rehabil. 24, 430–438. doi: 10.1097/HTR.0b013e3181c133e9

Dima, D., Jogia, J., and Frangou, S. (2014). Dynamic causal modeling of load-dependent modulation of effective connectivity within the verbal working memory network. Hum. Brain Mapp. 35, 3025–3035. doi: 10.1002/hbm.22382

Dobryakova, E., Boukrina, O., and Wylie, G. R. (2015). Investigation of information flow during a novel working memory task in individuals with traumatic brain injury. Brain Connect. 5, 433–441. doi: 10.1089/brain.2014.0283

Duff, S. J., and Hampson, E. (2000). A beneficial effect of estrogen on working memory in postmenopausal women taking hormone replacement therapy. Horm. Behav. 38, 262–276. doi: 10.1006/hbeh.2000.1625

Dunning, D. L., Westgate, B., and Adlam, A.-L. R. (2016). A meta-analysis of working memory impairments in survivors of moderate-to-severe traumatic brain injury. Neuropsychology 30, 811–819. doi: 10.1037/neu0000285

Ellis, M. U., DeBoard Marion, S., McArthur, D. L., Babikian, T., Giza, C., Kernan, C. L., et al. (2016). The UCLA study of children with moderate-to-severe traumatic brain injury: event-related potential measure of interhemispheric transfer time. J. Neurotrauma 33, 990–996. doi: 10.1089/neu.2015.4023

Engle, R. W. (2002). Working memory capacity as executive attention. Curr. Dir. Psychol. Sci. 11, 19–23. doi: 10.1111/1467-8721.00160

Engle, R. W., and Kane, M. J. (2004). “Executive attention, working memory capacity, and a two-factor theory of cognitive control,” in The Psychology of Learning and Motivation: Advances in Research and Theory , ed. B. H. Ross (New York, NY: Elsevier), 145–199. doi: 10.1016/S0079-7421(03)44005-X

Farrer, T. J. (2017). Encyclopedia of Geropsychology , ed. N. A. Pachana. Singapore: Springer. doi: 10.1007/978-981-287-080-3

CrossRef Full Text

Fiebig, F., and Lansner, A. (2017). A spiking working memory model based on hebbian short-term potentiation. J. Neurosci. 37, 83–96. doi: 10.1523/JNEUROSCI.1989-16.2016

Friston, K., Moran, R., and Seth, A. K. (2013). Analysing connectivity with granger causality and dynamic causal modelling. Curr. Opin. Neurobiol. 23, 172–178. doi: 10.1016/j.conb.2012.11.010

Gorman, S., Barnes, M. A., Swank, P. R., Prasad, M., Cox, C. S., and Ewing-Cobbs, L. (2016). Does processing speed mediate the effect of pediatric traumatic brain injury on working memory? Neuropsychology 30, 263–273. doi: 10.1037/neu0000214

Gorman, S., Barnes, M. A., Swank, P. R., Prasad, M., and Ewing-Cobbs, L. (2012). The effects of pediatric traumatic brain injury on verbal and visual-spatial working memory. J. Int. Neuropsychol. Soc. 18, 29–38. doi: 10.1017/S1355617711001251

Gottwald, B., Wilde, B., Mihajlovic, Z., and Mehdorn, H. M. (2004). Evidence for distinct cognitive deficits after focal cerebellar lesions. J. Neurol. Neurosurg. Psychiatry 75, 1524–1531. doi: 10.1136/jnnp.2003.018093

Grot, S., Légaré, V. P., Lipp, O., Soulières, I., Dolcos, F., and Luck, D. (2017). Abnormal prefrontal and parietal activity linked to deficient active binding in working memory in schizophrenia. Schizophr. Res. 188, 68–74. doi: 10.1016/j.schres.2017.01.021

Guye, S., and von Bastian, C. C. (2017). Working memory training in older adults: bayesian evidence supporting the absence of transfer. Psychol. Aging 32, 732–746. doi: 10.1037/pag0000206

Haller, S., Montandon, M.-L., Rodriguez, C., Moser, D., Toma, S., Hofmeister, J., et al. (2017). Caffeine impact on working memory-related network activation patterns in early stages of cognitive decline. Neuroradiology 59, 387–395. doi: 10.1007/s00234-017-1803-5

Haller, S., Rodriguez, C., Moser, D., Toma, S., Hofmeister, J., Sinanaj, I., et al. (2013). Acute caffeine administration impact on working memory-related brain activation and functional connectivity in the elderly: a BOLD and perfusion MRI study. Neuroscience 250, 364–371. doi: 10.1016/j.neuroscience.2013.07.021

Hedden, T., and Gabrieli, J. D. E. (2004). Insights into the ageing mind: a view from cognitive neuroscience. Nat. Rev. Neurosci. 5, 87–96. doi: 10.1038/nrn1323

Heinzel, S., Rimpel, J., Stelzel, C., and Rapp, M. A. (2017). Transfer effects to a multimodal dual-task after working memory training and associated neural correlates in older adults – a pilot study. Front. Hum. Neurosci. 11:85. doi: 10.3389/fnhum.2017.00085

Hillary, F. G., Medaglia, J. D., Gates, K., Molenaar, P. C., Slocomb, J., Peechatka, A., et al. (2011). Examining working memory task acquisition in a disrupted neural network. Brain 134, 1555–1570. doi: 10.1093/brain/awr043

Hsu, H.-L., Chen, D. Y.-T., Tseng, Y.-C., Kuo, Y.-S., Huang, Y.-L., Chiu, W.-T., et al. (2015). Sex differences in working memory after mild traumatic brain injury: a functional MR imaging study. Radiology 276, 828–835. doi: 10.1148/radiol.2015142549

Humphreys, M. S., Bain, J. D., and Pike, R. (1989). Different ways to cue a coherent memory system: a theory for episodic, semantic, and procedural tasks. Psychol. Rev. 96, 208–233. doi: 10.1037/0033-295X.96.2.208

Janowsky, J. S., Chavez, B., and Orwoll, E. (2000). Sex steroids modify working memory. J. Cogn. Neurosci. 12, 407–414. doi: 10.1162/089892900562228

Jimura, K., Chushak, M. S., Westbrook, A., and Braver, T. S. (2017). Intertemporal decision-making involves prefrontal control mechanisms associated with working memory. Cereb. Cortex doi: 10.1093/cercor/bhx015 [Epub ahead of print].

Joseph, J. E., Swearingen, J. E., Corbly, C. R., Curry, T. E., and Kelly, T. H. (2012). Influence of estradiol on functional brain organization for working memory. Neuroimage 59, 2923–2931. doi: 10.1016/j.neuroimage.2011.09.067

Karbach, J., and Verhaeghen, P. (2014). Making working memory work: a meta-analysis of executive control and working memory training in younger and older adults. Psychol. Sci. 25, 2027–2037. doi: 10.1177/0956797614548725

Kim, C., Kroger, J. K., Calhoun, V. D., and Clark, V. P. (2015). The role of the frontopolar cortex in manipulation of integrated information in working memory. Neurosci. Lett. 595, 25–29. doi: 10.1016/j.neulet.2015.03.044

Klaassen, E. B., De Groot, R. H. M., Evers, E. A. T., Snel, J., Veerman, E. C. I., Ligtenberg, A. J. M., et al. (2013). The effect of caffeine on working memory load-related brain activation in middle-aged males. Neuropharmacology 64, 160–167. doi: 10.1016/j.neuropharm.2012.06.026

Kovacs, K., and Conway, A. R. A. (2016). Process overlap theory: a unified account of the general factor of intelligence. Psychol. Inq. 27, 151–177. doi: 10.1080/1047840X.2016.1153946

Le, T. M., Borghi, J. A., Kujawa, A. J., Klein, D. N., and Leung, H.-C. (2017). Alterations in visual cortical activation and connectivity with prefrontal cortex during working memory updating in major depressive disorder. Neuroimage 14, 43–53. doi: 10.1016/j.nicl.2017.01.004

Liu, Z.-X., Glizer, D., Tannock, R., and Woltering, S. (2016). EEG alpha power during maintenance of information in working memory in adults with ADHD and its plasticity due to working memory training: a randomized controlled trial. Clin. Neurophysiol. 127, 1307–1320. doi: 10.1016/j.clinph.2015.10.032

Ma, L., Steinberg, J. L., Hasan, K. M., Narayana, P. A., Kramer, L. A., and Moeller, F. G. (2012). Working memory load modulation of parieto-frontal connections: evidence from dynamic causal modeling. Hum. Brain Mapp. 33, 1850–1867. doi: 10.1002/hbm.21329

Maehler, C., and Schuchardt, K. (2016). Working memory in children with specific learning disorders and/or attention deficits. Learn. Individ. Differ. 49, 341–347. doi: 10.1016/j.lindif.2016.05.007

Mandalis, A., Kinsella, G., Ong, B., and Anderson, V. (2007). Working memory and new learning following pediatric traumatic brain injury. Dev. Neuropsychol. 32, 683–701. doi: 10.1080/87565640701376045

Manktelow, A. E., Menon, D. K., Sahakian, B. J., and Stamatakis, E. A. (2017). Working memory after traumatic brain injury: the neural basis of improved performance with methylphenidate. Front. Behav. Neurosci. 11:58. doi: 10.3389/fnbeh.2017.00058

Miller, G. A., Galanter, E., and Pribram, K. H. (1960). Plans and the Structure of Behavior. New York, NY: Henry Holt and Company. doi: 10.1037/10039-000

Miyake, A., and Shah, P. (eds). (1999). Models of Working Memory: Mechanisms of Active Maintenance and Executive Control. New York, NY: Cambridge University Press. doi: 10.1017/CBO9781139174909

Mongillo, G., Barak, O., and Tsodyks, M. (2008). Synaptic theory of working memory. Science 319, 1543–1546. doi: 10.1126/science.1150769

Moore, A. B., Li, Z., Tyner, C. E., Hu, X., and Crosson, B. (2013). Bilateral basal ganglia activity in verbal working memory. Brain Lang. 125, 316–323. doi: 10.1016/j.bandl.2012.05.003

Murty, V. P., Sambataro, F., Radulescu, E., Altamura, M., Iudicello, J., Zoltick, B., et al. (2011). Selective updating of working memory content modulates meso-cortico-striatal activity. Neuroimage 57, 1264–1272. doi: 10.1016/j.neuroimage.2011.05.006

Nadebaum, C., Anderson, V., and Catroppa, C. (2007). Executive function outcomes following traumatic brain injury in young children: a five year follow-up. Dev. Neuropsychol. 32, 703–728. doi: 10.1080/87565640701376086

Nadler, R. T., and Archibald, L. M. D. (2014). The assessment of verbal and visuospatial working memory with school age canadian children. Can. J. Speech Lang. Pathol. Audiol. 38, 262–279.

Nakagawa, S., Takeuchi, H., Taki, Y., Nouchi, R., Sekiguchi, A., Kotozaki, Y., et al. (2016). Sex-related differences in the effects of sleep habits on verbal and visuospatial working memory. Front. Psychol. 7:1128. doi: 10.3389/fpsyg.2016.01128

Nissim, N. R., O’Shea, A. M., Bryant, V., Porges, E. C., Cohen, R., and Woods, A. J. (2017). Frontal structural neural correlates of working memory performance in older adults. Front. Aging Neurosci. 8:328. doi: 10.3389/fnagi.2016.00328

Oren, N., Ash, E. L., Tarrasch, R., Hendler, T., Giladi, N., and Shapira-Lichter, I. (2017). Neural patterns underlying the effect of negative distractors on working memory in older adults. Neurobiol. Aging 53, 93–102. doi: 10.1016/j.neurobiolaging.2017.01.020

Osaka, M., Osaka, N., Kondo, H., Morishita, M., Fukuyama, H., Aso, T., et al. (2003). The neural basis of individual differences in working memory capacity: an fMRI study. Neuroimage 18, 789–797. doi: 10.1016/S1053-8119(02)00032-0

Owen, A. M., McMillan, K. M., Laird, A. R., and Bullmore, E. (2005). N-back working memory paradigm: a meta-analysis of normative functional neuroimaging studies. Hum. Brain Mapp. 25, 46–59. doi: 10.1002/hbm.20131

Owens, J. A., Spitz, G., Ponsford, J. L., Dymowski, A. R., Ferris, N., and Willmott, C. (2017). White matter integrity of the medial forebrain bundle and attention and working memory deficits following traumatic brain injury. Brain Behav. 7:e00608. doi: 10.1002/brb3.608

Perbal, S., Couillet, J., Azouvi, P., and Pouthas, V. (2003). Relationships between time estimation, memory, attention, and processing speed in patients with severe traumatic brain injury. Neuropsychologia 41, 1599–1610. doi: 10.1016/S0028-3932(03)00110-6

Perlstein, W. M., Cole, M. A., Demery, J. A., Seignourel, P. J., Dixit, N. K., Larson, M. J., et al. (2004). Parametric manipulation of working memory load in traumatic brain injury: behavioral and neural correlates. J. Int. Neuropsychol. Soc. 10, 724–741. doi: 10.1017/S1355617704105110

Pham, A. V., and Hasson, R. M. (2014). Verbal and visuospatial working memory as predictors of children’s reading ability. Arch. Clin. Neuropsychol. 29, 467–477. doi: 10.1093/arclin/acu024

Phillips, N. L., Parry, L., Mandalis, A., and Lah, S. (2017). Working memory outcomes following traumatic brain injury in children: a systematic review with meta-analysis. Child Neuropsychol. 23, 26–66. doi: 10.1080/09297049.2015.1085500

Rees, K., Allen, D., and Lader, M. (1999). The influences of age and caffeine on psychomotor and cognitive function. Psychopharmacology 145, 181–188. doi: 10.1007/s002130051047

Reuter-Lorenz, P. A., and Cappell, K. A. (2008). Neurocognitive ageing and the compensation hypothesis. Curr. Dir. Psychol. Sci. 17, 177–182. doi: 10.1111/j.1467-8721.2008.00570.x

Reuter-Lorenz, P. A., and Park, D. C. (2010). Human neuroscience and the aging mind : a new look at old problems. J. Gerontol. Psychol. Sci. 65, 405–415. doi: 10.1093/geronb/gbq035

Rieck, J. R., Rodrigue, K. M., Boylan, M. A., and Kennedy, K. M. (2017). Age-related reduction of BOLD modulation to cognitive difficulty predicts poorer task accuracy and poorer fluid reasoning ability. Neuroimage 147, 262–271. doi: 10.1016/j.neuroimage.2016.12.022

Rodriguez Merzagora, A. C., Izzetoglu, M., Onaral, B., and Schultheis, M. T. (2014). Verbal working memory impairments following traumatic brain injury: an fNIRS investigation. Brain Imaging Behav. 8, 446–459. doi: 10.1007/s11682-013-9258-8

Rose, N. S., LaRocque, J. J., Riggall, A. C., Gosseries, O., Starrett, M. J., Meyering, E. E., et al. (2016). Reactivation of latent working memories with transcranial magnetic stimulation. Science 354, 1136–1139. doi: 10.1126/science.aah7011

Rotzer, S., Loenneker, T., Kucian, K., Martin, E., Klaver, P., and von Aster, M. (2009). Dysfunctional neural network of spatial working memory contributes to developmental dyscalculia. Neuropsychologia 47, 2859–2865. doi: 10.1016/j.neuropsychologia.2009.06.009

Schneider-Garces, N. J., Gordon, B. A., Brumback-Peltz, C. R., Shin, E., Lee, Y., Sutton, B. P., et al. (2010). Span, CRUNCH, and beyond: working memory capacity and the aging brain. J. Cogn. Neurosci. 22, 655–669. doi: 10.1162/jocn.2009.21230

Schöning, S., Engelien, A., Kugel, H., Schäfer, S., Schiffbauer, H., Zwitserlood, P., et al. (2007). Functional anatomy of visuo-spatial working memory during mental rotation is influenced by sex, menstrual cycle, and sex steroid hormones. Neuropsychologia 45, 3203–3214. doi: 10.1016/j.neuropsychologia.2007.06.011

Silvanto, J. (2017). Working memory maintenance: sustained firing or synaptic mechanisms? Trends Cogn. Sci. 21, 152–154. doi: 10.1016/j.tics.2017.01.009

Stegmayer, K., Usher, J., Trost, S., Henseler, I., Tost, H., Rietschel, M., et al. (2015). Disturbed cortico–amygdalar functional connectivity as pathophysiological correlate of working memory deficits in bipolar affective disorder. Eur. Arch. Psychiatry Clin. Neurosci. 265, 303–311. doi: 10.1007/s00406-014-0517-5

Treble, A., Hasan, K. M., Iftikhar, A., Stuebing, K. K., Kramer, L. A., Cox, C. S., et al. (2013). Working memory and corpus callosum microstructural integrity after pediatric traumatic brain injury: a diffusion tensor tractography study. J. Neurotrauma 30, 1609–1619. doi: 10.1089/neu.2013.2934

Vallat-Azouvi, C., Weber, T., Legrand, L., and Azouvi, P. (2007). Working memory after severe traumatic brain injury. J. Int. Neuropsychol. Soc. 13, 770–780. doi: 10.1017/S1355617707070993

Vartanian, O., Jobidon, M.-E., Bouak, F., Nakashima, A., Smith, I., Lam, Q., et al. (2013). Working memory training is associated with lower prefrontal cortex activation in a divergent thinking task. Neuroscience 236, 186–194. doi: 10.1016/j.neuroscience.2012.12.060

Wang, S., and Gathercole, S. E. (2013). Working memory deficits in children with reading difficulties: memory span and dual task coordination. J. Exp. Child Psychol. 115, 188–197. doi: 10.1016/j.jecp.2012.11.015

Wylie, G. R., Freeman, K., Thomas, A., Shpaner, M., OKeefe, M., Watts, R., et al. (2015). Cognitive improvement after mild traumatic brain injury measured with functional neuroimaging during the acute period. PLoS One 10:e0126110. doi: 10.1371/journal.pone.0126110

Ziaei, M., Salami, A., and Persson, J. (2017). Age-related alterations in functional connectivity patterns during working memory encoding of emotional items. Neuropsychologia 94, 1–12. doi: 10.1016/j.neuropsychologia.2016.11.012

Ziemus, B., Baumann, O., Luerding, R., Schlosser, R., Schuierer, G., Bogdahn, U., et al. (2007). Impaired working-memory after cerebellar infarcts paralleled by changes in bold signal of a cortico-cerebellar circuit. Neuropsychologia 45, 2016–2024. doi: 10.1016/j.neuropsychologia.2007.02.012

Zylberberg, J., and Strowbridge, B. W. (2017). Mechanisms of persistent activity in cortical circuits: possible neural substrates for working memory. Annu. Rev. Neurosci. 40, 603–627. doi: 10.1146/annurev-neuro-070815-014006

Keywords : working memory, neuroscience, psychology, cognition, brain, central executive, prefrontal cortex, review

Citation: Chai WJ, Abd Hamid AI and Abdullah JM (2018) Working Memory From the Psychological and Neurosciences Perspectives: A Review. Front. Psychol. 9:401. doi: 10.3389/fpsyg.2018.00401

Received: 24 November 2017; Accepted: 09 March 2018; Published: 27 March 2018.

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*Correspondence: Aini Ismafairus Abd Hamid, [email protected]

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Article contents

Working memory.

• Tom Hartley Tom Hartley University of York
• , and  Graham J. Hitch Graham J. Hitch University of York
• https://doi.org/10.1093/acrefore/9780190236557.013.768
• Published online: 19 October 2022

Working memory is an aspect of human memory that permits the maintenance and manipulation of temporary information in the service of goal-directed behavior. Its apparently inelastic capacity limits impose constraints on a huge range of activities from language learning to planning, problem-solving, and decision-making. A substantial body of empirical research has revealed reliable benchmark effects that extend to a wide range of different tasks and modalities. These effects support the view that working memory comprises distinct components responsible for attention-like control and for short-term storage. However, the nature of these components, their potential subdivision, and their interrelationships with long-term memory and other aspects of cognition, such as perception and action, remain controversial and are still under investigation. Although working memory has so far resisted theoretical consensus and even a clear-cut definition, research findings demonstrate its critical role in both enabling and limiting human cognition and behavior.

• short-term memory
• serial order
• intelligence

Introduction

The term working memory refers to human memory functions that serve to maintain and manipulate temporary information. There is believed to be a limited capacity to support these functions which combine to play a key role in cognitive processes such as thinking and reasoning, problem-solving, and planning. A common illustration is mental calculation which typically involves maintaining some initial numerical information whilst carrying out a series of arithmetical operations on parts and maintaining any interim results. However, the range of activities that depend on working memory is very much wider than that example might suggest. Thus, perception and action can also depend critically on maintaining and manipulating temporary information, as for instance when identifying a familiar constellation in the night sky, or when preparing a meal.

Information about a stimulus remains available for a few seconds after it is perceived (short-term memory) but without active maintenance it rapidly becomes inaccessible ( Peterson & Peterson, 1959 ; Posner & Konick, 1966 ). Conceptually, working memory extends short-term memory by adding the active, attentional processes required to hold information in mind and to manipulate that information in the service of goal-directed behavior.

The short-term storage required for working memory can be distinguished from long-term memory, which is concerned with more permanent information acquired through learning or experience and includes declarative memory (retention of factual information and events) and procedural memory (underpinning skilled behavior; see Cohen & Squire, 1980 ). Notably, and in contrast to short-term memory, these forms of long-term memory are passive in the sense that, once acquired, memory for facts, events, and well-learned skills can persist over very long periods without moment-to-moment awareness. For example, a vocabulary of many thousands of words, including the relationship between their spoken forms and meanings, can be retained effortlessly over a lifetime. Similarly, once acquired through practice, complex and initially challenging behaviors such as swimming or riding a bicycle can become almost automatic and can be carried out with relatively little conscious control.

In early models of the human memory system (e.g., Atkinson & Shiffrin, 1968 ; see Logie, 1996 ) short-term memory was seen as a staging post or gateway to long-term memory, and it was recognized that it could also support more complex operations, such as reasoning, thus acting as a working memory. Subsequent research has attempted to refine the concept of working memory, characterizing its functional role, limits, and substructure, and distinguishing the processes involved in maintenance and manipulation of information from the storage systems with which they interact.

It has proven difficult, however, to disentangle working memory function from other aspects of cognition with which it overlaps. First, as described in more detail in the section “ Substructure and Relationship to Other Aspects of Cognition ,” many current accounts view the mechanisms of working memory as contributing to other perhaps more fundamental functions such as attention, long-term memory, perception, action, and representation. It is also notable that many informal descriptions of working memory emphasize consciousness and awareness as key features. Intuitively, many working memory functions are accessible to consciousness, and concepts such as mental manipulation, rehearsal, and losing track of information through inattention are subjectively encountered as characteristics of the conscious mind. Of course, by definition, people cannot be subjectively aware of any unconscious contributions to working memory (although they can potentially be inferred from behavior). Some theorists have argued that working memory is central to conscious thought (e.g., Baars, 2005 ; Carruthers, 2017 ), while other empirical researchers have sought to demonstrate nonconscious processes operating in what would typically be considered working memory tasks (e.g., Hassin et al., 2009 ; Soto et al., 2011 ). It is not clear whether, how, or to what extent consciousness is essential for working memory functions, or whether indeed the definition of working memory ought to include, or avoid, aspects of conscious experience. This article steers away from the topic, but the current status of the debate is captured in reviews such as Persuh et al. (2018) . Overall, it is difficult to precisely delineate the boundaries of working memory, whether with other cognitive functions or with consciousness and awareness; in philosophical terms it may not constitute a “natural kind” ( Gomez-Lavin, 2021 ).

These challenges make it difficult to establish a clear-cut and uncontroversial definition of working memory itself, its function, and substructure. Yet it is clear that working memory describes a cluster of related abilities that play a critical role in everyday thinking, placing important constraints on what we can and cannot do. Research on the topic has proved fruitful and although there remain many theoretical controversies about how working memory should be defined and analyzed, these mainly relate to the way in which its operations and substrates can be usefully subdivided, and their interrelationships with other cognitive systems such as those responsible for long-term memory and attention (see Logie et al., 2021 for in-depth discussion).

The following sections begin by identifying relatively uncontroversial characteristics of working memory and its temporal and capacity limits before outlining the main theoretical perspectives on the structure of working memory and its relationship to other forms of cognition. This is followed by a summary of the main experimental tasks and key empirical observations which underpin current understanding. Finally, a brief discussion of the importance of working memory beyond the laboratory is provided.

Temporal Limits

It is broadly agreed that its temporary or labile character is a defining characteristic of working memory. In contrast with established declarative and procedural memories that can be retained indefinitely, recently presented novel information is typically lost after a few seconds unless actively maintained. This active maintenance of short-term memory in order to complete a task is one of the core functions of working memory. As discussed further (see “ Limiting Mechanisms ”), it is less clear how such information is lost over time, or whether forgetting is strictly linked to the passage of time (decay) or merely correlated with it (for example, through an accumulation of interfering information). Nonetheless the vulnerability of short-term memory to degradation over time constrains the uses to which it can be put. Active maintenance processes include rehearsal—covertly subvocalizing verbal material, and attentional refreshing—selectively attending to an item that has not yet become inactive (see e.g., Camos et al., 2009 ). These active processes are themselves limited by the modality and quantity of the stored material, so that for instance subvocal rehearsal is disrupted by speaking aloud at the same time (“articulatory suppression”; Murray, 1967 ), and attentional refreshing can only be directed at a limited number of items in a given period of time ( Camos et al., 2018 ). Even though such active maintenance processes extend the temporal limits of short-term memory, when they do so at the cost of limited attentional resources, this reduces the availability of those resources for other goals.

Capacity Limits

It is also agreed that the limited capacity of working memory is a defining characteristic; in subjective terms, only a limited number of items can be “held in mind” at once. For example, in the classic digit span test of short-term memory capacity, participants are asked to briefly store, and then recall in order, arbitrary sequences of digits of gradually increasing length. In this type of task, accurate performance is typically only possible for very short sequences of up to three or four items beyond which errors of ordering become ever more frequent. Memory span is defined as the sequence length at which recall is correct half the time and is found to be between six and seven for digits, and even less for items such as unrelated words ( Crannell & Parrish, 1957 ). Similar capacity constraints are evident in nonverbal tasks requiring the recall of spatial sequences or the locations or visual properties of objects in spatial arrays. For instance, in the Corsi Block task, participants follow an assessor in tapping out a sequence of blocks in a tabletop array or a sequence of highlighted squares on a computer display. In the standard task, nine blocks are used in a fixed configuration and healthy participants can only recall sequences of around six taps even when tested immediately after presentation ( Corsi, 1972 ; Milner, 1971 ). Such tasks are helpful in identifying the fundamental capacity constraints on short-term memory but working memory capacity is also constrained by the active processes that maintain and manipulate information. This is typically assessed using complex span tasks which measure how many items can be held in mind while carrying out an attention-demanding concurrent task, leading to far lower estimates than simple spans ( Daneman & Carpenter, 1980 ). Similarly, participants show greatly reduced performance on a backward digit span task where mental manipulation is required to reverse the original sequence at recall. (Interestingly the Corsi span is the same in both directions; Kessels et al., 2008 ). Notably, forward and backward digit span and Corsi Block tasks are all used in the clinical assessment of neuropsychological patients as well as in research studies, highlighting the importance of working memory capacity in characterizing healthy and impaired cognitive function.

Just as the temporal limits of short-term memory can be extended by active maintenance processes, its capacity limits can be mitigated through strategic processing. Although it is clear that the number of items that can be stored in working memory is limited, there is some flexibility about what constitutes an item. For example, the sequence “1-0-0” might constitute three digits or might be represented as a single item, “hundred.” The possibility of more efficient forms of coding depends on interactions with long-term memory and can be exploited strategically to extend working memory capacity through “chunking” ( Miller, 1956 ). Thus, for an IT professional, the sequence “CPUBIOSPC” is more easily maintained as the familiar acronyms “CPU,” “BIOS,” and “PC” than as an arbitrary sequence of 10 letters.

While the previous example exploits long-term knowledge, even arbitrary grouping can extend the capacity of working memory, for example, in the immediate serial recall of verbal sequences, performance is improved when items are presented in groups. A spoken sequence of digits like “352-168” (i.e., with a pause between the two groups of digits) is recalled more easily than the ungrouped sequence “352168” ( Ryan, 1969 ). Again, this effect can be deployed strategically, and there is evidence that participants spontaneously group verbal material in memory.

More generally, prior learning and experience can not only expand effective storage capacity but can also contribute to efficient active processing operations. For example, children may initially use a counting-on strategy to perform simple sums such as 2 + 3 = 5, but later typically learn arithmetic number facts that automate such operations, in turn permitting more demanding mental arithmetic to be carried out within working memory ( Raghubar et al., 2010 ). In the extreme, expert calculators may collect extraordinarily large “mental libraries” of number facts ( Pesenti et al., 1999 ). Another powerful strategy for extending working memory capacity is seen in expert abacus operators who in mental calculation are able to use visual imagery to internalize algorithms learned from using the physical device ( Stigler, 1984 ).

Limiting Mechanisms

Despite the clear consensus that limited capacity and duration are defining characteristics of working memory, distinguishing it from other forms of memory and learning, there is less agreement about the mechanisms through which information is limited and forgotten.

In one account, the ultimate capacity limits of the system are determined by its access to a limited number of discrete slots, each of which can be used to hold a chunk of information ( Cowan, 2001 ; Luck & Vogel, 1997 ). However, an alternative and increasingly influential view is that working memory has access to a continuous resource which can be flexibly deployed to support a greater number of chunks or items on the one hand, or greater fidelity and precision on the other ( Bays & Husain, 2008 ; see Ma et al., 2014 for discussion).

The loss of information from working memory over time can similarly be attributed to different mechanisms, although here they do not amount to mutually exclusive models of the same phenomenon. One potential mechanism is decay, assumed to be a fundamental property of the substrate of short-term memory, through which information is lost due to the passage of time alone. In this view the attentional/executive component of working memory is typically deployed to extend its capacity by strategically (but effortfully) refreshing or rehearsing the content of short-term memory before it decays irretrievably. A further potential mechanism is interference. In this account, memory traces are prone to be confused with, or gradually corrupt one another. Several current models incorporate a combination of decay and interference ( Baddeley et al., 2021 ; Barrouillet & Camos, 2021 ; Cowan et al., 2021 ; Vandierendonck, 2021 ), while Oberauer (2021) stands out in rejecting time-based forgetting and maintenance processes, proposing in their place loss due to interference, and requiring a process dedicated to the active removal of outdated information from working memory.

Substructure and Relationship to Other Aspects of Cognition

Because it is linked to such a wide range of cognitive capacities, it can be difficult to clearly distinguish mechanisms of working memory from those of its specialized subcomponents or of general-purpose cognitive mechanisms which contribute to nonmemory functions. There is a broad consensus that working memory involves the interaction of an active process (corresponding to “attention” or “executive control”) with a substrate that can represent the content of memory and thus act as a short-term store. Authors disagree, or are sometimes agnostic, as to the extent to which these components can be usefully subdivided and the degree to which they are uniquely involved in working memory or more generally in cognition. Authors also differ in the emphasis they put on different modalities and tasks. These different emphases may sometimes mask a deeper consensus in which models are complementary rather than incompatible ( Miyake & Shah, 1999 ).

Although the term working memory had already been applied to the use of short-term memory in goal-directed behavior ( Atkinson & Shiffrin, 1968 ), it was the influential work of Baddeley and Hitch ( Baddeley, 1986 ; Baddeley & Hitch, 1974 ), that introduced the separation of attentional control processes (governed by a “central executive”) and short-term storage systems (thought of as “buffers,” i.e., distinct and specialized systems). They further identified a distinction between verbal and visual buffers which were subject to different forms of disruption and appeared to use distinct codes. In particular, verbal information could be stored in a speech-based system (termed the “phonological loop”), in which similar sounding items were more likely to be confused and which was disrupted by concurrent articulation. This work led to the development of the multicomponent model, which subsequently incorporated a richer characterization of the visuo-spatial store (the “visuospatial sketchpad,” see e.g., Baddeley & Logie, 1999 ; Logie, 1995) and, later, an additional store—the “episodic buffer” which holds amodal information and interacts with episodic long-term memory ( Baddeley, 2000 ). The possibility of further substructure within these core components is also recognized (e.g., Logie, 1995 on distinguishing visual and spatial subcomponents; see also Logie et al., 2021 on the possibility of multiple substrates within a multicomponent perspective).

An alternative view, the embedded processes model put forward by Cowan (1999) , is that working memory can be seen as the controlled, temporary activation of long-term memory representations, with access to awareness being limited to three to four items or chunks. A key distinction with the multicomponent model hangs on whether working memory relies on a distinct substrate (as implied by the term “buffer”), or whether the substrate is shared with long-term memory. Oberauer (2002) similarly identifies working memory with activated representations in long-term memory. In this account, the activated region forms a concentric structure within which a subset of individual chunks inside a “region of direct access” compete to be selected as the focus of attention.

Other more recent theoretical accounts have also emphasized the role of attentional control in determining the limits of working memory. For example, Engle (2002) regarded capacity constraints as reflecting the limited ability to control domain-general executive attention in situations where there is the potential for interference among conflicting responses. The time-based resource sharing account ( Barrouillet & Camos, 2004 ) highlights the need to balance the active refreshing of short-term with concurrent processing demands. In this view, constraints arise from the necessary trade-off between maintenance and manipulation, both of which rely on common attentional resources.

Many theoretical approaches to working memory do not follow Baddeley and Hitch in identifying modality-specific substrates for the temporary storage of information and assume instead a unitary system in which many different types of feature can be represented (e.g., Cowan et al., 2021 ; Oberauer, 2021 ). In such accounts, modality-specific phenomena are attributed to differences in the extent to which such features overlap within and between modalities. On the other hand, some authors acknowledge the possibility that there may be many alternative substrates, and that even within a modality further subdivisions may be possible. So, for example substrates supporting memory of verbal/linguistic content might further distinguish auditory-verbal, lexical, and semantic levels of representation ( Barnard, 1985 ; Martin, 1993 ).

Neuroscientific investigations have tended to support the consensus idea of a broad separation between executive and attentional control processes on the one hand, and (often modality-specific) stores on the other, but if anything have highlighted even more extensive overlap of the neural substrate of working memory with other cognitive functions including sensory–perceptual and action–motor representation, and greater granularity and fractionation of function within both storage and control systems. This led Postle (2006) to argue that working memory should be seen as an emergent property of the mind and brain rather than a specialized system in its own right:

Working memory functions arise through the coordinated recruitment, via attention, of brain systems that have evolved to accomplish sensory-, representation-, and action-related functions. ( Postle, 2006 ), p. 23

Even in this view it is clear that the mechanisms of working memory (however they overlap with other cognitive functions) involve the interaction of distinct components (at minimum “attention” is distinguished from sensory/representation and action-related function, and these latter functions may also be further subdivided).

Empirical Investigation and Key Findings

A variety of tasks have been developed to investigate working memory in the laboratory. These tasks, of course, always require participants to briefly retain some novel information, often the identity of a set of items which might be visual (for example, colored shapes) or verbal (digits, words, letters). However, they vary quite considerably in the extent to which they require memory for the structure of the set (such as, for verbal stimuli, their order or the spatial layout of an array of items), the degree to which they place an ongoing or concurrent load on memory and attention, and the precision with which sensory and perceptual properties of the individual items must be represented. An excellent overview of these techniques and associated benchmark findings can be found in Oberauer et al. (2018) .

In an item recognition task, participants determine whether a specific item was in a set (a sequentially presented list or simultaneously displayed array) that they previously studied ( McElree & Dosher, 1989 ). In probed recall , they are provided with a cue that uniquely specifies a given item from a previously presented set, which they are then required to recall ( Fuchs, 1969 ). In free recall tasks, typically employed with verbal stimuli, participants are presented with an ordered list, but are allowed to recall the items in any order ( Postman & Phillips, 1965 ), whereas in serial recall ( Jahnke, 1963 ) they are required to retain the original order of presentation.

The preceding tasks place increasing demands on short-term memory for the structure as well as the content of the presented stimuli, but place relatively little requirement for attention or the manipulation of memory content. To address these aspects of working memory, a range of additional tasks have been developed. In complex span tasks the to-be-remembered items are interleaved with a processing task, placing a greater concurrent load on the attentional system ( Daneman & Carpenter, 1980 ). In the N-back task , items are presented rapidly and continuously, with the participant being required to decide whether each new item repeats one encountered exactly n-items earlier in the sequence; to do this they must not only maintain the order of the previous n-items, but also manage the capacity-limited short-term memory resource as every new item arrives. These demands become increasingly taxing as the value of n increases, again giving an indication of the effects of load on performance or, since it is particularly amenable to neuroimaging, brain activity (see Owen et al., 2005 for review). 1 As mentioned, the manipulation requirements of serial recall can be increased by reversing the order in which items are to be recalled. More involved forms of mental manipulation are explicitly tested in memory updating paradigms ( Morris & Jones, 1990 ), within which, after being presented with an array or description, participants are instructed to carry out a sequence of operations before retrieving the result.

To assess its fidelity over brief intervals, tasks that require memory for detailed properties of the items are useful. In change detection tasks (e.g., Luck & Vogel, 1997 ), participants are required to respond to alterations in the stimulus (typically a visually presented array) between presentation and testing. These alterations can be made arbitrarily small, thus testing the precision of the underlying memory representation. Going beyond recognition -like responses to change, in continuous reproduction or delayed estimation tasks , participants are asked to recall continuous features of the stimuli such as the precise color or orientation of a shape within a previously-studied array (e.g., Bays & Husain, 2008 ). These tasks allow researchers to go beyond the question of whether information is merely retained or lost; they can be used to characterize and quantify the quality of the underlying representation, which in turn can shed light on the potential trade-off between capacity and precision in working memory.

The preceding tasks provide a very useful set of tools for investigating working memory in the laboratory. To investigate the structure and operation of the system, experiments typically manipulate characteristics of the items to be stored, and often employ concurrent tasks devised to selectively disrupt putative components or processes. In their standard forms, the individual items are treated as equally valuable or important, but it is also possible to cue specific items, locations, or serial positions in order to encourage participants to prioritize specific content (e.g., Hitch et al., 2020 ; Myers et al., 2017 ). Improved recall for such prioritized items can then reveal the operation of strategic processes. Overall, such manipulations show a range of replicable effects, not just on overall performance and response times, but also on patterns of error. In turn these benchmark effects have provided the impetus for current theories and provide important constraints for emerging computational models of working memory ( Oberauer et al., 2018 ).

Set Size and Retention Interval Effects

The most important effects relate to capacity and temporal limits that have already been discussed, and these apply across all applicable experimental paradigms and modalities. Specifically, in terms of capacity limits, task accuracy is impaired as the number of items (set size) is increased (response times also generally increase with set size), and in terms of temporal limits, accuracy declines monotonically with the duration of a delay between presentation and testing. The latter effect is reliably seen for both verbal and spatial materials when the retention interval is filled with a distracting task. It does not apply to unfilled delays in tasks with verbal materials, and only sometimes occurs with spatial materials. The difference between filled and unfilled delays forms part of the evidence in favor of the core working memory concept of active executive/attentional processes in sustaining otherwise fleeting short-term memories.

Primacy and Recency Effects

Another signature of working memory is that items are retrieved with greater accuracy if they are presented at the beginning (primacy) or end (recency) of a sequence relative to other items. The operation of primacy and recency effects is seen in immediate serial recall and other tasks where the presentation order is well-defined, and for both verbal and visuo-spatial content. This leads to a serial position curve (in which accuracy is plotted for each serial position in a list) with a characteristic bowed shape. The effect suggests that a shared or general serial ordering mechanism privileges access to these serial positions in an ordered list and/or impairs access to other serial positions. It is important to note that primacy and recency effects are also observed in the immediate free recall of lists of words when the capacity of working memory is greatly exceeded and where they may have a very different explanation (see e.g., Baddeley & Hitch, 1993 ).

Errors and Effects of Similarity

Working memory errors frequently involve confusion between items in the memory set. This is evident in a wide range of tasks (including variants of recognition, change-detection, and continuous reproduction tasks), but is perhaps clearest in immediate serial recall, where the most common forms of error involve the misordering of items. These errors most frequently involve local transpositions in which an item moves to a nearby list position, often exchanging with the item in that position. For example, a sequence like “D, F, E, O, P, Q” might be recalled as “D, F, O, E, P, Q.” Items are most likely to transpose to immediately adjacent list positions, with the probability of a transposition decreasing monotonically as the distance within the sequence increases. Note that there are fewer opportunities for local transpositions at the beginning and end of a sequence so the locality constraint on transpositions likely plays at least some role in primacy and recency effects.

In a verbal working memory task, when items from the memory set are confused with one another, they are most likely to be confused with phonologically similar items making performance for lists of similar sounding items poorer than for phonologically distinct items. In serial recall, this effect manifests itself as an increased tendency for phonologically similar items to transpose with one another, so that in the preceding example, items “D,” “E” and “P” (because they rhyme) would be more likely to transpose with one another than items “F,” “O,” and “Q.” Although these similarity effects are largely reported in verbal paradigms, analogous findings are sometimes observed with visual materials (for example, a sequence of similar colored shapes is harder to reconstruct than a sequence of distinctively colored shapes; Jalbert et al., 2008 ).

The analysis of errors and confusion has been critical in understanding the nature of representation in verbal working memory (for example, demonstrating the importance of speech-based rather than semantic codes), in developing the concept of the phonological loop, and in developing computational models which account for these findings in terms of underpinning serial ordering mechanisms.

Individual Differences and Links With Other Facets of Cognition

Speaking to questions about the relationship between working memory and other aspects of cognition, another set of benchmark findings is concerned with correlations between performance on working memory tasks and other measures. In particular, working memory is correlated with measures of attention and fluid intelligence (the capacity to solve novel problems independent of prior learning; see e.g., Engle, 2002 ) suggesting that all three constructs involve common resources. There is consensus that aspects of attention contribute to working memory, but attention is also relevant to tasks that make minimal demands on memory. At the same time, working memory plays an important role in problem solving in the absence of relevant prior learning, but it can also be applied to tasks that do not involve complex problems. This suggests a hierarchical relationship in which limited cognitive resources (i.e., attention) are applied to maintain and manipulate information in memory (attention + short-term memory = working memory) in the context of demanding problems (working memory + problem solving = fluid intelligence).

This somewhat simplistic sketch of the relationship between constructs omits the contribution of long-term memory and learning to working memory. That contribution is evident in several empirical phenomena. For example, the beneficial effect of chunking on recall often depends on familiarity with the chunks, as in the examples given previously. It is easily overlooked that the familiarity of the materials themselves is also important. For example, familiar words are recalled much better than nonwords ( Hulme et al., 1991 ) suggesting that words act as specialized phonological/semantic “chunks.” Similarly, grammatical sentences are recalled better than arbitrarily ordered lists or jumbled sentences ( Brener, 1940 ). The word–nonword and sentence superiority effects show that well-learned constraints on serial order (whether through syntax or phonotactics) can benefit recall. A related phenomenon, the Hebb repetition effect ( Hebb, 1961 ), can be seen in the laboratory: immediate serial recall for a specific random list gradually improves over successive trials when it becomes more familiar through being repeatedly but covertly presented interleaved among other lists.

The Importance of Working Memory

The laboratory tasks and benchmark findings outlined in the section “ Empirical Investigation and Key Findings ” have established its key characteristics, but the practical significance of working memory extends well beyond these phenomena into everyday cognition and learning. Notably the limits of working memory constrain what we can think about on a moment-to-moment basis and hence how quickly we can learn and what we can ultimately understand. An appreciation of the impact of working memory and its limitations is thus vitally important in the context of education (see e.g., Alloway & Gathercole, 2006 for a review). For example, individual differences in the capacity of phonological storage in verbal working memory are reciprocally linked to vocabulary acquisition in early childhood; children’s ability to repeat nonwords at age four (i.e., unfamiliar phonological sequences) predicts their vocabulary a year later. In turn, the emergence of vocabulary (i.e., phonological chunks) is associated with later improvements in nonword repetition ( Gathercole et al., 1992 ). It is not hard to imagine that this process amplifies the initial effect of variation in capacity, affecting literacy and then more advanced learning (potentially well beyond language abilities) that depends on reading. Working memory can similarly exert an influence on the emergence of numeracy and through it more advanced skills in arithmetic and mathematics. For example, kindergartners’ performance on a backward digit span task predicts their scores on a mathematics test a year later ( Gersten et al., 2005 ). In addition to these effects on the acquisition of foundational skills such as literacy and numeracy, working memory is important in maintaining and manipulating the information needed to carry out complex tasks in the classroom. Thus, students with lower working memory capacity can have difficulty retaining and following instructions ( Gathercole et al., 2008 ) again potentially hampering their ability to build more advanced skills and knowledge. Because of its critical involvement in classroom learning, working memory plays a central role in Cognitive Load Theory” ( Sweller, 2011 ) an influential educational framework which aims to incorporate principles derived from the architecture of human cognition into teaching methods.

Many measures of short-term memory and working memory show marked year-on-year improvement in childhood, with developmental change likely reflecting the maturation of several components that underpin performance ( Gathercole, 1999 ; Gathercole et al., 2004 ). These include changes in processes such as verbal recoding, subvocal rehearsal, the activation of temporary information and executive attentional control ( Camos & Barrouillet, 2011 ; Cowan et al., 2002 ; Hitch & Halliday, 1983 ). As might be expected given the centrality of working memory in the acquisition of language and numeracy, developmental disorders are commonly associated with reduced short-term or working memory capacity. Prominent examples include dyslexia ( Berninger et al., 2008 ), developmental language disorder ( Archibald & Gathercole, 2006 ; Montgomery et al., 2010 ), and dyscalculia ( Fias et al., 2013 ; McLean & Hitch, 1999 ). However, the nature of any causal role for working memory in developmental disorders has been controversial (see e.g., Masoura, 2006 ).

In adulthood, working memory capacity continues to limit the bandwidth that is available for cognitive operations, for example affecting planning and decision-making ( Gilhooly, 2005 ; Hinson et al., 2003 ). As we grow older, working memory capacity tends to decline, and there are some indications that this is associated with failing attention and greater vulnerability to distraction ( Hasher & Zacks, 1988 ; McNab et al., 2015 ; Park & Payer, 2006 ) rather than a mere reversal of earlier developmental gains. Across the entire lifespan, as it waxes and wanes, working memory plays an important part in shaping our daily experience.

Given its central role in constraining human cognitive abilities, extensive efforts have been made to develop interventions that can improve working memory, for example through computerized training programs. However, these efforts have so far met with limited success. Some working memory tasks show improvements with practice, but these effects tend to reflect near or intermediate transfer , specific to the trained task or (often closely-related) direct measures of working memory, rather than far transfer extending to more general improvements in other tasks thought to depend on working memory, such as reading comprehension or arithmetic ( Melby-Lervåg et al., 2016 ; Owen et al., 2010 ; Sala & Gobet, 2017 ). It has been argued that near and intermediate transfer effects arise through improvements in task-specific efficiency via refinement of strategies and long-term memory support (e.g., chunking) whereas more general benefits and far transfer would be expected to depend on the underlying capacity of attentional and storage systems ( von Bastian & Oberauer, 2014 ). The absence of clear evidence for far transfer despite such extensive research thus suggests that working memory capacity limits are a fundamental and unalterable feature of the human cognitive system.

Although it is perhaps premature to rule out the possibility of interventions that achieve increased working memory capacity, it appears at present that it can only be extended in specific contexts through more specialized training with particular tasks and materials. Paradoxically, this resistance to more general training may be what makes working memory so important; to the extent that its capacity limits are unavoidable, working memory helps to determine the scope of human cognition and spurs us to find strategies, technologies and cultural tools that allow us to go beyond them.

In conclusion, through the development of a powerful toolkit of experimental methods and of replicable empirical phenomena, the study of working memory function has provided many useful insights into interactions between attention and short-term memory. On the one hand these interactions can be used strategically to enhance goal-directed behavior and long-term learning while on the other they provide fundamental limits on cognition across the lifespan. Ongoing controversy over the structure of working memory relates to the difficulty in isolating these interactions from other facets of cognition, but there is little doubt about their importance in governing what we can and cannot do.

• Alloway, T. P. , & Gathercole, S. E. (2006). How does working memory work in the classroom? Education Research and Reviews , 1 (4), 134–139.
• Archibald, L. M. D. , & Gathercole, S. E. (2006). Short-term and working memory in specific language impairment . International Journal of Language & Communication Disorders , 41 (6), 675–693.
• Atkinson, R. C. , & Shiffrin, R. M. (1968). Human memory: A proposed system and its control processes In K. W. Spence & J. T. Spence (Eds.), The psychology of learning and motivation: Advances in research and theory (Vol. 2, pp. 89–195). Academic Press.
• Baars, B. J. (2005). Global workspace theory of consciousness: Toward a cognitive neuroscience of human experience . In S. Laureys (Ed.), Progress in brain research (Vol. 150, pp. 45–53). Elsevier.
• Baddeley, A. (1986). Working memory (pp. xi, 289). Clarendon Press.
• Baddeley, A. (2000). The episodic buffer: A new component of working memory ? Trends in Cognitive Sciences , 4 (11), 417–423.
• Baddeley, A. D. , & Hitch, G. (1974). Working memory . In G. H. Bower (Ed.), Psychology of learning and motivation (Vol. 8, pp. 47–89). Academic Press.
• Baddeley, A. D. , & Hitch, G. (1993). The recency effect: Implicit learning with explicit retrieval? Memory & Cognition , 21 (2), 146–155.
• Baddeley, A. , Hitch, G. , & Allen, R. (2021). A multicomponent model of working memory . In R. Logie , V. Camos , & N. Cowan (Eds.), Working memory: The state of the science . Oxford University Press.
• Baddeley, A. , & Logie, R. (1999). Working memory: The multiple-component model . In A. Miyake & P. Shah (Eds.), Models of working memory: Mechanisms of active maintenance and executive control (pp. 28–61). Cambridge University Press.
• Barnard, P. (1985). Interacting cognitive subsystems: A psycholinguistic approach to short-term memory. In A. W. Ellis (Ed.), Progress in the psychology of language (Vol. 2, pp. 197–258).
• Barrouillet, P. , & Camos, V. (2004). Time constraints and resource sharing in adults’ working memory spans. Journal of Experimental Psychology: General , 133 , 83–100.
• Barrouillet, P. , & Camos, V. (2021). The time-based resource-sharing model of working memory . In R. Logie , V. Camos , & N. Cowan (Eds.), Working memory . Oxford University Press.
• Bays, P. M. , & Husain, M. (2008). Dynamic shifts of limited working memory resources in human vision . Science , 321 (5890), 851–854.
• Berninger, V. W. , Raskind, W. , Richards, T. , Abbott, R. , & Stock, P. (2008). A multidisciplinary approach to understanding developmental dyslexia within working-memory architecture: Genotypes, phenotypes, brain, and instruction . Developmental Neuropsychology , 33 (6), 707–744.
• Brener, R. (1940). An experimental investigation of memory span . Journal of Experimental Psychology , 26 (5), 467–482.
• Camos, V. , & Barrouillet, P. (2011). Developmental change in working memory strategies: From passive maintenance to active refreshing . Developmental Psychology , 47 (3), 898–904.
• Camos, V. , Johnson, M. , Loaiza, V. , Portrat, S. , Souza, A. , & Vergauwe, E. (2018). What is attentional refreshing in working memory? Annals of the New York Academy of Sciences , 1424 (1), 19–32.
• Camos, V. , Lagner, P. , & Barrouillet, P. (2009). Two maintenance mechanisms of verbal information in working memory . Journal of Memory and Language , 61 (3), 457–469.
• Carruthers, P. (2017). The centered mind: What the science of working memory shows us about the nature of human thought (Reprint ed.). Oxford University Press.
• Cohen, N. J. , & Squire, L. R. (1980). Preserved learning and retention of pattern-analyzing skill in amnesia: Dissociation of knowing how and knowing that . Science , 210 (4466), 207–210.
• Corsi, P. (1972). Memory and the medial temporal region of the brain . McGill University.
• Cowan, N. (1999). An Embedded-Processes Model of working memory . In A. Miyake & P. Shah (Eds.), Models of working memory: Mechanisms of active maintenance and executive control (pp. 62–101). Cambridge University Press.
• Cowan, N. (2001). The magical number 4 in short-term memory: A reconsideration of mental storage capacity . Behavioral and Brain Sciences , 24 (1), 87–114.
• Cowan, N. , Morey, C. C. , & Naveh-Benjamin, M. (2021). An embedded-processes approach to working memory: How is it distinct from other approaches, and to what ends ? In R. Logie , V. Camos , & N. Cowan (Eds.), Working memory . Oxford University Press.
• Cowan, N. , Saults, J. S. , & Elliott, E. M. (2002). The search for what is fundamental in the development of working memory . In R. V. Kail & H. W. Reese (Eds.), Advances in child development and behavior (Vol. 29, pp. 1–49). JAI.
• Crannell, C. W. , & Parrish, J. M. (1957). A comparison of immediate memory span for digits, letters, and words . The Journal of Psychology: Interdisciplinary and Applied , 44 , 319–327.
• Daneman, M. , & Carpenter, P. A. (1980). Individual differences in working memory and reading . Journal of Verbal Learning and Verbal Behavior , 19 (4), 450–466.
• Engle, R. W. (2002). Working memory capacity as executive attention . Current Directions in Psychological Science , 11 (1), 19–23.
• Fias, W. , Menon, V. , & Szucs, D. (2013). Multiple components of developmental dyscalculia . Trends in Neuroscience and Education , 2 (2), 43–47.
• Fuchs, A. H. (1969). Recall for order and content of serial word lists in short-term memory . Journal of Experimental Psychology , 82 (1, Pt.1), 14–21.
• Gathercole, S. E. (1999). Cognitive approaches to the development of short-term memory . Trends in Cognitive Sciences , 3 (11), 410–419.
• Gathercole, S. E. , Durling, E. , Evans, M. , Jeffcock, S. , & Stone, S. (2008). Working memory abilities and children’s performance in laboratory analogues of classroom activities . Applied Cognitive Psychology , 22 (8), 1019–1037.
• Gathercole, S. E. , Pickering, S. J. , Ambridge, B. , & Wearing, H. (2004). The structure of working memory from 4 to 15 years of age . Developmental Psychology , 40 (2), 177–190.
• Gathercole, S. E. , Willis, C. S. , Emslie, H. , & Baddeley, A. D. (1992). Phonological memory and vocabulary development during the early school years: A longitudinal study . Developmental Psychology , 28 (5), 887–898.
• Gersten, R. , Jordan, N. C. , & Flojo, J. R. (2005). Early identification and interventions for students with mathematics difficulties . Journal of Learning Disabilities , 38 (4), 293–304.
• Gilhooly, K. J. (2005). Working memory and planning. The Cognitive Psychology of Planning , 71–88.
• Gomez-Lavin, J. (2021). Working memory is not a natural kind and cannot explain central cognition . Review of Philosophy and Psychology , 12 (2), 199–225.
• Hasher, L. , & Zacks, R. T. (1988). Working memory, comprehension, and aging: A review and a new view . In G. H. Bower (Ed.), The psychology of learning and motivation: Advances in research and theory (Vol. 22, pp. 193–225). Academic Press.
• Hassin, R. R. , Bargh, J. A. , Engell, A. D. , & McCulloch, K. C. (2009). Implicit working memory . Consciousness and Cognition , 18 (3), 665–678.
• Hebb, D. O. (1961). Distinctive features of learning in the higher animal. In J. F. Delafresnaye (Ed.), Brain mechanisms and learning (pp. 37–46). Blackwell.
• Hinson, J. M. , Jameson, T. L. , & Whitney, P. (2003). Impulsive decision making and working memory. Journal of Experimental Psychology: Learning, Memory, and Cognition , 29 (2), 298.
• Hitch, G. J. , Allen, R. J. , & Baddeley, A. D. (2020). Attention and binding in visual working memory: Two forms of attention and two kinds of buffer storage. Attention, Perception, & Psychophysics , 82 (1), 280–293.
• Hitch, G. J. , & Halliday, M. S. (1983). Working memory in children . Philosophical Transactions of the Royal Society of London. B, Biological Sciences , 302 (1110), 325–340.
• Hulme, C. , Maughan, S. , & Brown, G. D. A. (1991). Memory for familiar and unfamiliar words: Evidence for a long-term memory contribution to short-term memory span . Journal of Memory and Language , 30 (6), 685–701.
• Jahnke, J. C. (1963). Serial position effects in immediate serial recall . Journal of Verbal Learning and Verbal Behavior , 2 (3), 284–287.
• Jalbert, A. , Saint-Aubin, J. , & Tremblay, S. (2008). Short article: Visual similarity in short-term recall for where and when . Quarterly Journal of Experimental Psychology , 61 (3), 353–360.
• Kane, M. J. , Conway, A. R. , Miura, T. K. , & Colflesh, G. J. (2007). Working memory, attention control, and the N-back task: A question of construct validity. Journal of Experimental Psychology: Learning, Memory, and Cognition , 33 (3), 615.
• Kessels, R. P. C. , van den Berg, E. , Ruis, C. , & Brands, A. M. A. (2008). The backward span of the Corsi Block-Tapping Task and its association with the WAIS-III Digit Span . Assessment , 15 (4), 426–434.
• Logie R.H. (1995). Visuo-Spatial Working Memory . Psychology Press.
• Logie, R. H. (1996). The seven ages of working memory. In J. T. E. Richardson , R. W. Engle , L. Hasher , R. H. Logie , E. R. Stoltzfus , & R. T. Zacks (Eds.), Working memory and human cognition (pp. 31–65).
• Logie, R. , Camos, V. , & Cowan, N. (Eds.). (2021). Working memory: The state of the science (1st ed.). Oxford University Press.
• Luck, S. J. , & Vogel, E. K. (1997). The capacity of visual working memory for features and conjunctions . Nature , 390 (6657), 279–281.
• Ma, W. J. , Husain, M. , & Bays, P. M. (2014). Changing concepts of working memory . Nature Neuroscience , 17 (3), 347–356.
• Martin, C. (1993). Short-term memory and sentence processing: Evidence from neuropsychology. Memory & Cognition , 21 (2), 176–183.
• Masoura, E. V. (2006). Establishing the link between working memory function and learning disabilities. Learning Disabilities: A Contemporary Journal , 4 (2), 29–41.
• McElree, B. , & Dosher, B. A. (1989). Serial position and set size in short-term memory: The time course of recognition . Journal of Experimental Psychology: General , 118 (4), 346–373.
• McLean, J. F. , & Hitch, G. J. (1999). Working memory impairments in children with specific arithmetic learning difficulties . Journal of Experimental Child Psychology , 74 (3), 240–260.
• McNab, F. , Zeidman, P. , Rutledge, R. B. , Smittenaar, P. , Brown, H. R. , Adams, R. A. , & Dolan, R. J. (2015). Age-related changes in working memory and the ability to ignore distraction . Proceedings of the National Academy of Sciences , 112 (20), 6515–6518.
• Melby-Lervåg, M. , Redick, T. S. , & Hulme, C. (2016). Working memory training does not improve performance on measures of intelligence or other measures of “far transfer”: Evidence From a meta-analytic review . Perspectives on Psychological Science , 11 (4), 512–534.
• Miller, G. A. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information . Psychological Review , 63 (2), 81–97.
• Milner, B. (1971). Interhemispheric differences in the localization of psychological processes in man . British Medical Bulletin , 27 (3), 272–277.
• Miyake, A. , & Shah, P. (Eds.). (1999). Models of working memory: Mechanisms of active maintenance and executive control . Cambridge University Press.
• Montgomery, J. W. , Magimairaj, B. M. , & Finney, M. C. (2010). Working memory and specific language impairment: An update on the relation and perspectives on assessment and treatment . American Journal of Speech-Language Pathology , 19 (1), 78–94.
• Morris, N. , & Jones, D. M. (1990). Memory updating in working memory: The role of the central executive . British Journal of Psychology , 81 (2), 111–121.
• Murray, D. J. (1967). The role of speech responses in short-term memory . Canadian Journal of Psychology/Revue Canadienne de Psychologie , 21 (3), 263.
• Myers, N. E. , Stokes, M. G. , & Nobre, A. C. (2017). Prioritizing information during working memory: Beyond sustained internal attention . Trends in Cognitive Sciences , 21 (6), 449–461.
• Oberauer, K. (2002). Access to information in working memory: Exploring the focus of attention. Journal of Experimental Psychology: Learning, Memory, and Cognition , 28 , 411–421.
• Oberauer, K. (2021). Towards a theory of working memory: From metaphors to mechanisms . In R. Logie , V. Camos , & N. Cowan (Eds.), Working memory . Oxford University Press.
• Oberauer, K. , Lewandowsky, S. , Awh, E. , Brown, G. D. A. , Conway, A. , Cowan, N. , Donkin, C. , Farrell, S. , Hitch, G. J. , Hurlstone, M. J. , Ma, W. J. , Morey, C. C. , Nee, D. E. , Schweppe, J. , Vergauwe, E. , & Ward, G. (2018). Benchmarks for models of short-term and working memory . Psychological Bulletin , 144 (9), 885–958.
• Owen, A. M. , Hampshire, A. , Grahn, J. A. , Stenton, R. , Dajani, S. , Burns, A. S. , Howard, R. J. , & Ballard, C. G. (2010). Putting brain training to the test . Nature , 465 (7299), 775–778.
• Owen, A. M. , McMillan, K. M. , Laird, A. R. , & Bullmore, E. (2005). N-back working memory paradigm: A meta-analysis of normative functional neuroimaging studies . Human Brain Mapping , 25 (1), 46–59.
• Park, D. C. , & Payer, D. (2006). Working memory across the adult lifespan . In Lifespan cognition: Mechanisms of change (pp. 128–142). Oxford University Press.
• Persuh, M. , LaRock, E. , & Berger, J. (2018). Working memory and consciousness: The current state of play . Frontiers in Human Neuroscience , 12 .
• Pesenti, M. , Seron, X. , Samson, D. , & Duroux, B. (1999). Basic and exceptional calculation abilities in a calculating prodigy: A case study . Mathematical Cognition , 5 (2), 97–148.
• Peterson, L. , & Peterson, M. J. (1959). Short-term retention of individual verbal items . Journal of Experimental Psychology , 58 (3), 193–198.
• Posner, M. I. , & Konick, A. F. (1966). On the role of interference in short-term retention . Journal of Experimental Psychology , 72 (2), 221–231.
• Postle, B. R. (2006). Working memory as an emergent property of the mind and brain . Neuroscience , 139 (1), 23–38.
• Postman, L. , & Phillips, L. W. (1965). Short-term temporal changes in free recall . Quarterly Journal of Experimental Psychology , 17 (2), 132–138.
• Raghubar, K. P. , Barnes, M. A. , & Hecht, S. A. (2010). Working memory and mathematics: A review of developmental, individual difference, and cognitive approaches . Learning and Individual Differences , 20 (2), 110–122.
• Ryan, J. (1969). Grouping and short-term memory: Different means and patterns of grouping . Quarterly Journal of Experimental Psychology , 21 (2), 137–147.
• Sala, G. , & Gobet, F. (2017). Working memory training in typically developing children: A meta-analysis of the available evidence . Developmental Psychology , 53 (4), 671–685.
• Soto, D. , Mäntylä, T. , & Silvanto, J. (2011). Working memory without consciousness . Current Biology , 21 (22), R912–R913.
• Stigler, J. W. (1984). “Mental abacus”: The effect of abacus training on Chinese children’s mental calculation . Cognitive Psychology , 16 (2), 145–176.
• Sweller, J. (2011). Cognitive load theory . In J. P. Mestre & B. H. Ross (Eds.), Psychology of learning and motivation (Vol. 55, pp. 37–76). Academic Press.
• Vandierendonck, A. (2021). Multicomponent working memory system with distributed executive control . In R. Logie , V. Camos , & N. Cowan (Eds.), Working memory . Oxford University Press.
• von Bastian, C. C. , & Oberauer, K. (2014). Effects and mechanisms of working memory training: A review . Psychological Research , 78 (6), 803–820.

1. However, note that, in at least one study ( Kane et al., 2007 ) n-back performance correlated only weakly with a measure of span, suggesting that, despite face validity, it may tax distinct cognitive resources.

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• Collection: Theoretical review with commentaries: attention and working memory

Review Article

Working memory and attention – a conceptual analysis and review.

• Klaus Oberauer

There is broad agreement that working memory is closely related to attention. This article delineates several theoretical options for conceptualizing this link, and evaluates their viability in light of their theoretical implications and the empirical support they received. A first divide exists between the concept of attention as a limited resource, and the concept of attention as selective information processing. Theories conceptualizing attention as a resource assume that this resource is responsible for the limited capacity of working memory. Three versions of this idea have been proposed: Attention as a resource for storage and processing, a shared resource for perceptual attention and memory maintenance, and a resource for the control of attention. The first of these three is empirically well supported, but the other two are not. By contrast, when attention is understood as a selection mechanism, it is usually not invoked to explain the capacity limit of working memory – rather, researchers ask how different forms of attention interact with working memory, in two areas. The first pertains to attentional selection of the contents of working memory, controlled by mechanisms of filtering out irrelevant stimuli, and removing no-longer relevant representations from working memory. Within working memory contents, a single item is often selected into the focus of attention for processing. The second area pertains to the role of working memory in cognitive control. Working memory contributes to controlling perceptual attention – by holding templates for targets of perceptual selection – and controlling action – by holding task sets to implement our current goals.

• Working memory
• Cognitive Control

There is a broad consensus that working memory and attention are intimately linked ( Awh, Jonides, & Reuter-Lorenz, 1998 ; Baddeley, 1993 ; Chun, 2011 ; Cowan, 1995 ; Gazzaley & Nobre, 2012 ; Kane, Bleckley, Conway, & Engle, 2001 ; Kiyonaga & Egner, 2014 ; Oberauer, 2009 ; Olivers, 2008 ). But what is it that we agree upon? Both working memory and attention can be conceptualized in different ways, resulting in a broad array of theoretical options for linking them. The purpose of this review is to propose a map for organizing these theoretical options, delineate their implications, and to evaluate the evidence for each of them.

The meaning of the concept working memory (WM) depends on the theory in which the concept figures. The definitions reviewed by Cowan ( 2017 ) differ primarily in the substantive assumptions they include (e.g., whether or not WM consists of multiple storage modules, and to what extent it includes long-term memory). Beyond these differences in theoretical assumptions, however, there is a broad consensus on what the term working memory refers to: The mechanisms and processes that hold the mental representations currently most needed for an ongoing cognitive task available for processing.

The meanings of the term attention are more diverse, as they reflect distinctions not only of definitions but also of different referents of the term: Attention is not a unitary entity ( Chun, Golomb, & Turk-Browne, 2011 ). Conceptualizations of attention can be distinguished along several dimensions that provide a coordinate system for our conceptual map. A first distinction pertains to how attention is defined. One definition of attention characterizes it as a limited resource for information processing (e.g., Wickens, 1980 ). Another concept of attention is as a process of (or mechanism for) selection of information to be processed with priority (e.g., Chun et al., 2011 ; Desimone & Duncan, 1995 ). These two concepts of attention play different roles in theorizing about working memory, and I will discuss them in turn below.

A second distinction pertains to what we attend to. I find it useful to distinguish the possible objects of attention along two dimensions (see Table 1 ). 1 First, we can distinguish between attention to our currently perceived environment (e.g., attention to visual objects or auditory streams) from attention to information currently not perceived, such as attention to remembered episodes or concepts that we think about. 2 Second, we can distinguish between attention to things and events in the world around us on the one hand, and attention to our own goals and (mental or overt) actions on the other. The latter form of attention includes selection of our current goal or task set and shielding it from distraction ( Kane & Engle, 2003 ; Monsell, 2003 ), selection of one of several possible actions ( Pashler, 1994 ), and monitoring of our actions and their outcomes ( Yeung, Botvinick, & Cohen, 2004 ).

A Taxonomy of Attention.

Note: Descriptions pertaining to attention as selection/prioritization are printed in regular font; descriptions pertaining to attention as a resource in italics.

A third distinction pertains to the forces that determine what we attend to – this is the distinction between controlled and automatic deployment of attention ( Shiffrin & Schneider, 1977 ). Attention is controlled when it is directed according to our current goals. The influence of current goals on attention is often referred to as “top-down”. Attention is automatic to the extent that its direction is influenced by forces independent of our current goals – these include the “bottom-up” attraction of attention by perceived properties of the stimuli (e.g., their “salience”) as well as influences of our learning history on what we attend to, for instance when attention is drawn to information that we have learned to be relevant ( Awh, Belopolsky, & Theeuwes, 2012 ; Theeuwes, 2018 ).

The concept of executive attention is often used when discussing the relation between attention and working memory. Executive attention is a term that is notoriously poorly defined ( Jurado & Rosselli, 2007 ). It is used on the one hand to refer to attention directed to one’s own goals and (mental or overt) actions, including response selection ( Szmalec, Vandierendonck, & Kemps, 2005 ), action planning, protecting the pursuit of our current goal from distractions and temptations, as well as switching from one task to another. On the other hand, executive attention is also used to refer to the top-down control of attention, including attention to things and events in the environment – for keeping our attention on the relevant stimuli or features and avoiding distraction by irrelevant ones, as in the Stroop task and the flanker task. As such, the term executive attention is used to denote one pole on each of two dimensions in my proposed taxonomy, one pertaining to the objects of attention (things and events in the world vs. our own goals and actions), the other pertaining to what determines the orientation of attention (controlled vs. automatic). The first meaning assigns executive attention a function in controlling our thoughts and actions (including what we attend to) whereas the second states that executive attention is itself controlled. One way to perhaps bring together the two meanings is by assuming that we attend to (i.e., select, assign resources to) our own goals and actions – including the action of attending to some object – in order to control them. Nevertheless, I find the term executive attention disquietingly ambiguous, and therefore will use instead the terms attention to (cognitive) action and controlled attention to refer to the two aspects of executive attention, respectively.

I organize the review by the two definitions of attention – as a resource or as a selection mechanism – because they have different implications for how attention and working memory are related. Within each section I will discuss the different objects of attention, and the different modes of control.

Attention as a Resource

The idea of attention as a resource is that the cognitive system has a limited resource that can be used for carrying out so-called attention-demanding processes. The resource is assumed to be a continuous quantity that can be split arbitrarily and allotted to different processes, depending on task demands. Processing efficiency (i.e., speed, accuracy) is a positive monotonic function of the amount of resource assigned to a process ( Navon & Gopher, 1979 ). The assumption that WM capacity reflects a limited resource has a long tradition ( Anderson, Reder, & Lebiere, 1996 ; Case, 1972 ; Just & Carpenter, 1980 ; Ma, Husain, & Bays, 2014 ). Authors linking WM to an attentional resource are endorsing the view that the limited capacity of WM reflects a limited resource, and that this resource also serves some (or all) functions commonly ascribed to attention. Three versions of this idea can be distinguished by which functions the attentional resource is assumed to be needed for: (1) storage and processing of information (e.g., Just & Carpenter, 1992 ), (2) perceptual attention and memory maintenance (e.g., Ester, Fukuda, May, Vogel, & Awh, 2014 ; Kiyonaga & Egner, 2014 ), or (3) the control of attention (e.g., Allen, Baddeley, & Hitch, 2006 ; Baddeley, 1993 , 1996 ; Lavie, 2005 ).

Attention for Storage and Processing

Many theorists discussing the relation between working memory and attention characterize attention as a limited resource for maintaining representations in an “active”, available state ( Cowan, 2005 ). Often this resource is assumed to be shared between “storage” and “processing” ( Case, Kurland, & Goldberg, 1982 ; Cowan et al., 2005 ; Just & Carpenter, 1992 ). According to this view, the same attentional resource is required for keeping representations available and for carrying out certain basic cognitive processes such as selecting a response to a stimulus. A prediction from this theory is that attention-demanding cognitive processes compete with concurrent storage ( Z. Chen & Cowan, 2009 ).

There are two variants of this theoretical idea. One is that a share of the resource needs to be continuously assigned to a representation to keep it in WM ( Case et al., 1982 ). The other is that attention is required directly only for processing, not storage. In this view attention indirectly contributes to memory maintenance because it is needed for refreshing WM representations, which would otherwise decay ( Barrouillet, Bernardin, & Camos, 2004 ). Barrouillet and colleagues further specify the resource required for refreshing as the limited resource for so-called central processes, such as response selection ( Barrouillet, Bernardin, Portrat, Vergauwe, & Camos, 2007 ). Dual-task studies with variants of the PRP (psychological refractory period) paradigm have established a strong capacity limit on central processes ( Pashler, 1994 ), which has been explained by a limited central-attentional resource ( Navon & Miller, 2002 ; Tombu & Jolicoeur, 2003 ).

Theorists linking WM to attention as resource commonly assume that there is a single, content-general attentional resource. It follows that storage and processing compete with each other whether or not they share any contents. This assumption leads to the prediction of dual-task costs when WM storage and processing demands from very different contents are combined with each other. There is considerable evidence confirming this prediction ( Chein, Moore, & Conway, 2011 ; Morey & Bieler, 2012 ; Saults & Cowan, 2007 ; Vergauwe, Barrouillet, & Camos, 2010 ), lending support to the notion that WM capacity is limited by an attentional resource. There is also evidence that storage and processing compete for central processing capacity: The extent to which maintenance in WM is impaired by concurrent processing is a monotonic function of cognitive load , defined as the proportion of time during which central attention is engaged by the processing demand ( Barrouillet et al., 2007 ).

One problem for the assumption of a shared resource for storage and processing is that, although a memory load reduces the efficiency of concurrent response-selection tasks, that dual-task cost diminishes substantially over the first few seconds of the retention interval ( Jolicoeur & Dell’Acqua, 1998 ; Thalmann, Souza, & Oberauer, 2019 ; Vergauwe, Camos, & Barrouillet, 2014 ), and is often not observed at all when there is an unfilled interval of a few seconds between encoding of the memory set and commencement of the processing task ( Hazeltine & Witfall, 2011 ; Klapp, Marshburn, & Lester, 1983 ; Oberauer, Demmrich, Mayr, & Kliegl, 2001 ). This observation has already led Klapp and colleagues ( 1983 ) to question the idea of a shared resource for storage and processing: To uphold this idea we would have to assume that the resource demand of maintenance dwindles to a negligible level within a few seconds. This would be compatible with the assumption that a central processing resource is required for short-term consolidation of information in working memory ( Jolicoeur & Dell’Acqua, 1998 ; Nieuwenstein & Wyble, 2014 ; Ricker & Hardman, 2017 ) but not with the assumption that a resource is needed for maintenance throughout the retention interval.

As mentioned above, the assumption of shared resources for storage and processing comes in two variants: The first, traditional one is that a representation needs a share of the resource assigned to it to be in WM, and the same resource is needed for carrying out cognitive operations. The second variant is that maintenance processing such as refreshing share a limited resource with other cognitive operations ( Barrouillet et al., 2004 ). The second variant rests on the premise that without refreshing the representations in WM decay – only on that assumption does the processing resource assigned to refreshing become essential for WM maintenance. The decay assumption, however, is probably not true, at least for verbal materials ( Oberauer & Lewandowsky, 2013 , 2014 ).

The first variant has a conceptual problem: Simultaneous maintenance and processing compete for a shared resource only until the processing task is completed – after that, the full resource can be re-assigned to the representations in WM. Why then should memory performance suffer from a concurrent processing task although memory is tested only after the processing task is done? (for a more detailed treatment see Oberauer, Farrell, Jarrold, & Lewandowsky, 2016 ). The problem is illustrated by a study that, according to the authors, reveals the neuronal basis of resource sharing: Watanabe and Funahashi ( 2014 ) recorded from multiple neurons in the lateral pre-frontal cortex (LPFC) while monkeys did a spatial attention task, a spatial WM task, or a dual-task combination of the two. The two tasks recruited largely overlapping LPFC neurons, which showed spatial selectivity when each task was done alone. While both tasks were done simultaneously, the LPFC neurons lost most of their spatial selectivity, and collectively their firing rate pattern contained less information about the attended location and the remembered location during that period. After the attention task was completed, however, the information about the location in memory was “reawakened” in the firing pattern of the LPFC neurons, reaching the same strength as in the single-task condition. The authors did observe a (small) performance decrement in the dual-task relative to the single-task condition, but that dual-task cost is not explained by their neural data – looking at the neural data, we would expect no detrimental effect on memory by the concurrent attention task.

To conclude, the assumption of a shared resource for memory retention and central processes has received much empirical support. At the same time, it is challenged by the finding that dual-task costs on processing speed tend to vanish over time, and – depending on the version endorsed – the lack of evidence for decay, and the problem of how to explain that the competition between processing and storage affects memory performance after the competition has ended.

Attention for Perception and Memory

A resource shared between “storage” and “processing” spans both sides of the distinction between attention to things and events (i.e., the information to be stored), and attention to goals and actions (i.e., to the task sets guiding the processing operations). We can also ask whether the same resource applies to both sides of another distinction, the one between perceptual attention and attention to not-perceived objects. Most task paradigms for studying WM require retention of information in the absence of perceptual input. There is evidence, however, that the limited capacity of WM applies not only to information in memory but equally to information still in view. Tsubomi, Fukuda, Watanabe, and Vogel ( 2013 ) measured the contralateral delay activity (CDA), a neural marker of the number of objects a person holds in visual WM ( Luria, Balaban, Awh, & Vogel, 2016 ; Vogel & Machizawa, 2004 ) while participants attended to a variable number of color patches still in view, or attempted to remember them after their offset. In both cases, the CDA amplitude increased with set size up to about 3 items and then levelled off. Individual CDA amplitudes correlated with performance on a test of one randomly selected item regardless of whether that item remained in view until the time of test or had to be retained in memory for a second.

The study of Tsubomi et al. ( 2013 ) shows striking similarities between the capacity limits for attending to perceptual stimuli and for maintaining stimuli in memory (see also Ester et al., 2014 ). Still, these two functions could rely on separate resources that happen to bear similarities to each other. If the same limited resource underlies perceptual attention and maintenance in WM, then demanding both at the same time should incur a substantial dual-task cost, such that when the load of one task is increased, performance on the other suffers. The evidence for this prediction is mixed. Fougnie and Marois ( 2006 ) found load-dependent dual-task costs when combining a visual WM task with a visual attention task (simultaneous tracking of multiple moving objects, or monitoring multiple parallel streams of rapidly presented visual stimuli for a target) but these costs were less than the cost of combining two visual WM tasks. Souza and Oberauer ( 2017 ) found only negligible dual-task costs when inserting a visual attention task (monitoring a stimulus for a subtle brightness change) in the retention interval of a visual WM task. Several studies investigated dual-task costs between WM and visual search. These dual-task costs increase with the load on each of the two tasks – as expected on the assumption of a shared resource – only when the contents of WM were spatial locations (for a review see Woodman & Chun, 2006 ). To conclude, although attending to perceptual information and maintaining information in WM after it disappeared from the environment have much in common, the evidence that they share a limited resource is not yet convincing.

Controlled Attention

The concept of attention as a limited resource is often linked specifically to controlled attention, whereas automatic attention is thought not to be resource demanding ( Schneider & Shiffrin, 1977 ; Shiffrin & Schneider, 1977 ). There are two ways in which this link can be spelled out: (a) Attention that is allocated in a controlled manner – according to “top down” influences from our current goals – underlies a resource limit but attention that is automatically attracted to some information independent of its relevance for our current goal does not underlie that resource limit. Stated in this way we face the awkward conclusion that allocating attention to the same object (e.g., a red traffic light in a street scene, or a word we hold in WM) does or does not rely on a limited resource depending on what forces led attention to that object. The same cognitive function – prioritizing processing of the attended information – would be resource consuming or not depending on how it was invoked.

In my view, a less awkward interpretation is: (b) Paying attention to an object does not require a resource per se – rather the process of controlling attention in a top-down manner consumes the limited resource. This interpretation reflects how Shiffrin and Schneider ( 1977, p. 156 ) explain why controlled processes are capacity limited: These processes need to be controlled by continuously paying attention to them, and attention cannot be allocated to more than one process at a time. In other words, the attentional resource imposes a bottleneck on the control processes, not on the controlled processes. The limitation is on how many different (cognitive or overt) actions we can attend to at the same time in order to control them. For instance, in visual search, perceptual attention can be drawn to some stimuli automatically, and theoretically there is no limit on how many such forces exert their pull in parallel. Perceptual attention can also be directed in a controlled manner – by attending to the action of deploying attention to visual stimuli – and this control process is limited to one action at a time. The limitation does not rest with the controlled attention – a limit on how many visual stimuli can be attended at the same time – but with the controlling attention.

This conception of an attentional resource differs from the preceding two. The notion of a resource for storage and processing and the idea of a shared attentional resource for perception and memory share the assumption that the resource is allocated to representations of objects and events that we perceive or hold in WM. In contrast, the “attentional control” idea assumes a resource for the control of what we attend to, and more generally, of what we think and do. These conceptualizations have different implications when we apply them to WM. For instance, consider a situation in which WM receives an overload of information, some of which is relevant and some of which is irrelevant. Examples of this scenario are the complex-span paradigm ( Daneman & Carpenter, 1980 ), in which to-be-remembered items alternate with stimuli to be processed but not retained, or the filtering paradigm ( Vogel, McCollough, & Machizawa, 2005 ), in which participants see an array of visual stimuli and need to remember a pre-defined subset (e.g., only the red objects). According to theories assuming a limited resource allocated to representations in WM, attention limits how much of the given information can be retained, and a separate parameter determines the filtering efficiency, that is, the extent to which the cognitive system manages to keep the distractor information out of WM, so that it does not consume part of the valuable storage resource. These theories predict that individuals with lower WM capacity maintain a smaller amount of both relevant and irrelevant information, but their proportion, reflecting filtering efficiency, should be independent of WM capacity. According to the controlled-attention view, by contrast, the attentional resource determines the filtering efficiency. Hence, individuals with lower WM capacity retain the same amount of information as those with higher capacity, but people differing in WM capacity differ in the ratio of relevant to irrelevant information that they retain.

Paradoxes lurk when we try to combine the two notions of attentional resources, assuming that the same limited resource is required for both storage and control: According to this fusion version of the attentional-resource idea, keeping some irrelevant piece of information out of WM, or removing it from WM, consumes attentional resource (because it is an act of control over what we attend to) and at the same time frees up attentional resource (because it reduces the amount of information that is held in WM). In the same manner, stopping a cognitive process costs attentional resource but at the same time frees up attentional resource. With such a conception, it becomes virtually impossible to say whether some cognitive process – such as filtering or deleting information from WM – renders a net cost or a net gain in resource. As a consequence, the theory becomes untestable. This problem needs to be kept in mind when attempts are made to reconcile the two versions of attentional-resource theories of WM (e.g., Cowan, Fristoe, Elliott, Brunner, & Saults, 2006 ). 3

If WM and the control of attention share a limited resource, we should expect substantial dual-task costs when an attention-control demand is combined with WM maintenance. Evidence for such a dual-task cost comes from studies demonstrating that a load on WM increases people’s susceptibility to distraction, for instance by the irrelevant stimuli in a flanker task ( Kelley & Lavie, 2011 ; Lavie, Hirst, de Fockert, & Viding, 2004 ). Interpretation of this result is complicated by the observation that only a verbal WM load increases the flanker effect – a visual WM load has the opposite effect ( Konstantinou, Beal, King, & Lavie, 2014 ; Konstantinou & Lavie, 2013 ). Konstantinou et al. ( 2014 ) explain this dissociation by assuming that visual WM contents place a load on a visual perceptual resource, and increasing the load on perceptual resources has been shown to reduce flanker interference ( Lavie, 2005 ). In contrast, verbal WM relies on rehearsal for maintenance, and rehearsal competes for a shared attentional-control resource with the control of visual attention. The latter assumption is at odds with the position of most other resource theorists, who assume that rehearsal requires little, if any such resource ( Baddeley, 1986 ; Camos, Lagner, & Barrouillet, 2009 ; Cowan, 2001 ). Other studies provide further evidence that a load on WM can both increase and decrease people’s distractability by a flanker stimulus during a perceptual comparison task: When the category of stimuli held in WM matched that of the targets of the comparison task (but not that of the flankers), the flanker compatibility effect increased, but when the WM contents matched the category of the flankers, and not the targets, then the flanker compatibility effect decreased under load compared to no load ( Kim, Kim, & Chun, 2005 ; Park, Kim, & Chun, 2007 ). Taken together, there is no convincing evidence that loading WM depletes a resource needed for the control of attention.

We can also ask whether concurrent demands on the control of attention impair performance in a WM task. This appears not to be the case. The effect of concurrent processing on memory is larger when the processing task requires more attention control (e.g., task switching vs. task repetition, incongruent vs. neutral Stroop trials), but that effect is entirely accounted for by the longer duration of response selection in the more difficult conditions ( Barrouillet, Portrat, & Camos, 2011 ; Liefooghe, Barrouillet, Vandierendonck, & Camos, 2008 ). Hence, the dual-task cost of concurrent processing for memory is a function of the demand on central attention for action selection, not the demand on the control of attention. Moreover, Lawrence, Myerson, Oonk, and Abrams ( 2001 ) found that when people had to make saccades to irrelevant locations during the retention interval, memory performance is impaired, in particular for spatial information. That effect was equally large for reflexive saccades towards a suddenly appearing target and for controlled anti-saccades away from a target, contrary to the assumption that the control of attention in the anti-saccade condition competes for WM resources. Bunting, Cowan, and Colflesh ( 2008 ) used a manual analog of the anti-saccade task as distractor activity during the retention interval, and found significantly worse performance in the anti-press than the pro-press condition in only 3 out of 12 experimental conditions.

A second prediction from the assumption that WM maintenance and controlled attention share a resource is that measures of the efficiency of the two should be correlated across individuals. This prediction has been tested with regard to two forms of control over the contents of WM ( Hasher, Zacks, & May, 1999 ): Filtering irrelevant stimuli at encoding so that they never enter WM, and removal of no-longer relevant stimuli from WM after they have been encoded. Support for the prediction comes from studies measuring filtering efficiency in visual change-detection tasks through the effect of irrelevant stimuli on the CDA ( Vogel et al., 2005 ). Individual differences in filtering efficiency are strongly correlated with accuracy in change detection ( Luria et al., 2016 ). However, when Mall, Morey, Wolff, and Lehnert ( 2014 ) measured filtering efficiency through behavioral indicators – the performance gain from being able to ignore half the stimuli in the array, and the proportion of time people fixated on locations of irrelevant stimuli during encoding and retention – they found no correlation with people’s WM capacity, measured through complex-span tasks. One possible interpretation is that controlled attention (as indexed by filtering) and WM maintenance share a resource that is not domain general but rather specific to visual stimuli. Removal efficiency has been measured through the speed with which people remove to-be-updated information from WM in an updating paradigm ( Ecker, Lewandowsky, & Oberauer, 2014 ). Whereas this first study showed no correlation of removal efficiency with WM capacity, a subsequent study measuring removal efficiency through a larger set of updating tasks observed a small positive correlation ( Singh, Gignac, Brydges, & Ecker, 2018 ). This result could reflect a shared resource for WM maintenance and attentional control. Alternatively, it could mean that people who efficiently remove no-longer relevant information from WM are better at reducing interference from that information in WM, which improves their ability to retrieve the relevant information ( Oberauer, Lewandowsky, Farrell, Jarrold, & Greaves, 2012 ).

Other research investigated the correlation between WM capacity and measures of attentional control outside the context of WM tasks, for instance the ability to attend to relevant and ignore irrelevant stimuli or features in perceptual decision tasks (e.g., the Stroop, flanker, or Simon task), the ability to suppress a strong action tendency (e.g., moving the eyes away from a suddenly appearing stimulus in the anti-saccade task), or the ability to stop an already prepared action (i.e., the stop-signal paradigm). Numerous studies have found positive correlations between WM capacity and these measures of attention control (e.g., Chuderski, 2014 ; McVay & Kane, 2012 ; Shipstead, Lindsey, Marshall, & Engle, 2014 ; Unsworth, 2015 ; Unsworth, Fukuda, Awh, & Vogel, 2014 ), whereas a few others failed to find such a relationship ( Keye, Wilhelm, Oberauer, & van Ravenzwaaij, 2009 ; Wilhelm, Hildebrandt, & Oberauer, 2013 ). Additional support comes from findings of a positive correlation between WM capacity and people’s self-reported mind wandering in response to thought probes during a cognitive task ( McVay & Kane, 2009 , 2012 ; Randall, Oswald, & Beier, 2014 ).

Taken together, the evidence for a close relation between WM and the control of attention is mixed. The most convincing evidence comes from correlational studies linking WM capacity to indicators of attention control from tasks without a memory demand. There is some evidence that WM capacity is also correlated with the efficiency of controlling the contents of WM through filtering and removal, but it is yet too weak and inconsistent to draw strong conclusions. This correlational evidence, however, can be explained without invoking the notion of a shared resource, as I’ll discuss below (in the section “How is WM related to the control of attention and action?”). The experimental evidence from dual-task costs speaks against competition between WM maintenance and attention control for a shared resource.

I have considered three theoretical options for spelling out the idea of WM as relying on an attentional resource: (1) a shared resource for “storage” and “processing”, (2) a shared resource for perceptual attention and WM, and (3) a shared resource for attention control and WM. Of these three, the first option has received the most convincing empirical support, but it also suffers from empirical challenges, and from the conceptual problem of explaining how the competition for resources between storage and processing can have an impact on memory performance after the competition is over. I do not see these challenges as fatal – it is probably still too early to announce the “demise” ( Klapp et al., 1983 ) of the idea that WM is limited by an attentional resource – but theorists working with this concept should aim to address these challenges. In the remainder of this article I discuss the relation of WM to attention from the perspective that attention is the selection and prioritization of information, which does not entail a commitment to a limited resource.

Attention as Selection

A different perspective on the relation between WM and attention emerges when attention is defined not as a resource but as a mechanism for selecting and prioritizing representations. In this perspective, attention does not explain the capacity limit of WM. Rather, we should consider WM as an instance of attention – specifically, WM is attention to memory representations. Holding a set of representations in WM means selecting them from among all the representations that our mind is capable of, thereby rendering them available as input for cognitive operations. As such, WM meets the definition of attention as a mechanism of selection ( Oberauer, 2009 ). In this perspective, the relationship between the concept of WM and the concept of attention is not an empirical but a conceptual one.

Nevertheless, we can ask several empirical questions about how WM is related to attention as a selection mechanism: (1) How is information selected into WM? (2) How is information selected within WM? (3) What is the relation between attention to memory and attention to perceived stimuli – are they the same, and if not, how do they influence each other? (4) How is WM related to the control of attention and action? I next address these questions in turn.

How is Information Selected into Working Memory?

Information can be selected to be brought into WM from perception or from long-term memory. This selection is to a large extent controlled: People are very good, though not perfect, at letting only relevant information into WM. Moreover, people also have control over which information to keep in WM and which to remove.

Filtering Perceptual Information. With regard to perceived information, perceptual attention arguably plays an important role in selecting which stimuli are encoded into WM. Stimuli that are known to be irrelevant from the start, and are easy to discriminate from relevant stimuli, can be filtered out very effectively ( Baddeley, Papagno, & Andrade, 1993 ), though not always perfectly ( Ueno, Allen, Baddeley, Hitch, & Saito, 2011 ; Vogel et al., 2005 ); children and older adults seem to have more difficulty with filtering irrelevant stimuli at encoding ( Sander, Werkle-Bergner, & Lindenberger, 2011 ). A question discussed in the context of visual WM is whether people can selectively encode relevant features but not irrelevant features of the same visual object. Some experiments show that relevant and irrelevant features of the same object have similar behavioral effects on memory performance ( Marshall & Bays, 2013 ) and attentional capture ( Gao et al., 2016 ; see the section on effects of WM on perceptual attention for an explanation of this effect). However, one fMRI study found that the relevant but not the irrelevant feature of a visual object could be reconstructed from the pattern of BOLD activity during the retention interval ( Yu & Shim, 2017 ). Logie, Brockmole, and Jaswal ( 2011 ) have tested the effects of changes in irrelevant features on change-detection accuracy and found that such changes impair performance for retention intervals up to about 2 s but not thereafter. They propose that irrelevant features are initially encoded and subsequently removed from WM. This could explain why irrelevant features are not detectable in the sluggish BOLD signal that aggregates information over several seconds.

Filtering could be accomplished by perceptual selection – not attending to the irrelevant stimuli – but it could also be a separate selection step, such that a stimulus, even though selected for perceptual attention, is not encoded into WM. The latter possibility would imply that perceptual attention might be necessary, but is not sufficient for encoding them into WM. Evidence for this possibility comes from several sources. A series of experiments by H. Chen and Wyble ( 2015a , 2015b ) used stimuli as attentional cues for a perceptual decision task, and after several trials inserted a surprise memory test for a feature of the cue. Although they have arguably attended to the cue because it was relevant for the decision task, people had poor memory for its features only a few seconds after its disappearance, suggesting that the stimulus, or at least the feature probed in the memory test, was not encoded into WM. When people expected the memory test, their performance was much better. In a related experiment H. Chen, Swan, and Wyble ( 2016 ) had participants visually track several moving target objects among distractors. To avoid confusing the targets with distractors participants had to continuously attend to them while they moved. Yet, in a surprise memory test they had little memory for the target’s colors.

A second source of evidence suggesting that attention is not sufficient to encode stimuli into WM comes from some of my experiments ( Oberauer, 2018 ): Participants saw six words presented one by one in different screen locations; each word was followed by a cue to remember or forget it. The cue appeared only after word offset so that people had to attend to each word in case they would have to remember it. I also varied the time interval between each forget cue and the onset of the next word to manipulate how much time people had to remove a to-be-forgotten word from WM. The to-be-forgotten words had no effect on memory performance regardless of the cue-word interval, implying that they did not contribute at all to the load on WM.

These findings could mean that information, although attended, is not encoded into WM. Alternatively, the visual stimuli of Chen and Wyble, or the to-be-forgotten words in my experiments, could be encoded into WM but then removed very quickly so that their accessibility, and their effect on WM load, was not measurable even a few seconds later (see the section below on Removal). Perhaps neurophysiological markers of WM load with high temporal resolution, such as the CDA, could be leveraged to distinguish between these possibilities.

One limitation for efficient filtering (or removal) arises when people have to process the distracting material. When participants in my experiments ( Oberauer, 2018 ) had to make a judgment on each word while it was on the screen, they could not entirely prevent encoding to-be-forgotten words into WM, though they were still able to diminish their effect on WM load relative to to-be-remembered words. Marshall and Bays ( 2013 ) found that comparing two stimuli during the retention interval of a visual WM task impaired WM performance as much as adding two more stimuli to the memory set, suggesting that encoding of these stimuli into WM could not be prevented at all.

Selective Retrieval from Long-Term Memory. Much of the information we process in WM comes from long-term memory. For the WM system to work effectively, it has to retrieve information from long-term memory selectively, so that only information useful for the current task enters WM ( Oberauer, 2009 ). A demonstration of the effectiveness of this gating mechanism comes from experiments investigating the effect of previously acquired long-term memories on WM performance ( Oberauer, Awh, & Sutterer, 2017 ). We had participants learn 120 associations between everyday objects and randomly selected colors. In a subsequent WM test they had to maintain three object-color conjunctions on each trial, and reproduce each object’s color by selecting it on a color wheel. Some of the objects in the WM test were objects for which they had learned an associated color before. These objects could reoccur in the WM test with their learned color – in which case retrieving the associated color should facilitate WM performance – whereas others reoccurred with a new random color – in which case retrieving the color from long-term memory should interfere with WM performance. We found evidence for proactive facilitation, but against proactive interference, implying that information from long-term memory is used if and only if the information in WM was so poor that drawing on long-term memory could only make things better.

Removal of Information from WM. The selection of which information to hold in WM is also controlled after encoding: Information no longer relevant must be rapidly removed so that it does not clutter WM ( Hasher et al., 1999 ). There is a body of evidence showing that people can selectively remove no-longer relevant information from WM (for a review see Lewis-Peacock, Kessler, & Oberauer, 2018 ).

Removing an entire memory set when replacing it with a new one is a seamless and rapid process, though – as filtering – it is not perfect: Traces of the old memory set remain in WM, creating some mild proactive interference when items in the two sets are similar to each other ( Ralph et al., 2011 ; Tehan & Humphreys, 1998 ), and a congruency benefit when the two sets partially overlap, sharing the same items in the same contexts ( Oberauer, Souza, Druey, & Gade, 2013 ). Removal of a single item from the current memory set has been isolated experimentally as a process involved in WM updating ( Ecker, Oberauer, & Lewandowsky, 2014 ). By contrast, removal is much less efficient when it comes to removing more than one item from a memory set but less than all of them: People find it difficult to remove a random subset of several items from a memory set. For instance, when informed, after encoding a list of six words, that the words in positions 2, 3, and 5 could be forgotten, there was no evidence that they did so – successful removal of a subset of three words was found only when they were already clearly marked as a separate subset at encoding ( Oberauer, 2018 ). In sum, the efficiency of removal is limited by the ability to discriminate between to-be-maintained and to-be-removed contents of WM.

To conclude, the WM system is equipped with very efficient – though not perfect – mechanisms for controlling its contents through filtering perceptual input, selectively retrieving information from LTM, and removing no-longer relevant materials. Through these selection processes the cognitive system manages to usually have only the most relevant information for the current goal in WM.

How is Information selected within WM?

Selecting information to be held in WM is a form of selection, but it not necessarily selection of one piece of information at the exclusion of all others: We often hold multiple separate items in WM simultaneously. Sometimes we have to select a single item from the set currently held in WM as the input for a process, or as the object of mental manipulation. Our ability to select individual items from the set currently held in WM points to a selection mechanism that I refer to as the focus of attention in WM ( Oberauer, 2002 ; Oberauer & Hein, 2012 ). Evidence for the operation of such a narrow selection mechanism within WM comes from three observations: (1) In short-term recognition tests the last-presented item in a list is accessed at a faster rate than preceding items, and this has been interpreted as showing that the last-encoded item remains in the focus of attention (for a review McElree, 2006 ). (2) When an item in WM is needed as input for a cognitive operation (e.g., adding or subtracting a number from a particular digit in WM), or when one item needs to be selected as the object of an updating operation (e.g., replacing an item in WM by a new stimulus), then operating on the same WM item again in the next step takes less time than selecting another item from the memory set for the next operation. This item-switch cost (or item-repetition benefit) has been explained by assuming that the object of a cognitive operation remains in the focus of attention after the operation has been completed, and therefore does not need to be selected again when the same object is required for the next operation ( Garavan, 1998 ; Oberauer, 2003 ). (3) After encoding a set of stimuli into WM, a retro-cue presented one to several seconds into the retention interval can guide attention to one item and thereby improve memory performance when that item is tested – often at the expense of performance when another item is tested ( Griffin & Nobre, 2003 ; Landman, Spekreijse, & Lamme, 2003 ; for a review see Souza & Oberauer, 2016 ).

Whereas most of these empirical demonstrations come from situations in which a single item in WM needs to be selected, it has been argued that the focus of attention can hold more than one item ( Gilchrist & Cowan, 2011 ). From the perspective of attention as selection, this should be feasible to the extent that selecting multiple items simultaneously does not undercut the purpose of selection. For instance, if the task is to update one out of several digits in WM through an arithmetic operation, selecting more than that one digit into the focus of attention would only lead to confusion – but if the task is to add two digits in WM together, selecting both of them into the focus of attention at the same time is arguably useful because then they could be used simultaneously as retrieval cues for the relevant arithmetic fact ( Oberauer, 2013 ). Another situation in which it is functional to select two items into the focus simultaneously is when two tasks must be carried out simultaneously, one on each item, and the two items are sufficiently different to not risk cross-talk between the two tasks ( Göthe, Oberauer, & Kliegl, 2016 ; Oberauer & Bialkova, 2011 ).

Using the retro-cue paradigm, neuroscience research has revealed a distinction between attended and unattended information in WM 4 : Whereas the attended information can be decoded from neural signals such as the pattern of BOLD activity over voxels, or the pattern of EEG activity over electrodes, the unattended information cannot – it remains neurally silent, but can be brought back into a neurally active state later by a retro-cue drawing attention to it ( LaRocque, Lewis-Peacock, Drysdale, Oberauer, & Postle, 2013 ; Lewis-Peacock, Drysdale, Oberauer, & Postle, 2011 ; Sprague, Ester, & Serences, 2016 ) or by an uninformative strong input to the cortex ( Rose et al., 2016 ; Wolff, Jochim, Akyürek, & Stokes, 2017 ). One recent study, however, paints a more differentiated picture: Decoding of orientations maintained in VWM from fMRI signals in visual cortex was again good for attended and absent for unattended items, but decoding from signals in parietal cortex (IPS and frontal eye fields) was equally good for both attended and unattended items – though much weaker than decoding of attended items in visual cortex ( Christophel, Iamshchinina, Yan, Allefeld, & Haynes, 2018 ).

Behavioral evidence shows that retro-cues can be used to select not just individual items but also subsets of several items within WM ( Oberauer, 2001 , 2005 ), and selection of a subset can be followed by selection of an item within that subset ( Oberauer, 2002 ). Therefore, we can distinguish three levels of selection in WM: (1) Selecting information to be in WM, constituting the current memory set, (2) selecting a subset of the memory set, and (3) selecting a single item from that subset. I have referred to these three levels as (1) the activated part of long-term memory, (2) the region of direct access, and (3) the focus of attention, respectively (see Oberauer, 2009 , for a detailed discussion of the 3-level framework and evidence supporting it; and Oberauer et al., 2013 , for a computational implementation). It is currently not clear whether more than one WM representation is neurally active (i.e., decodable from neural activity during the retention interval) at the same time, so we do not know whether the state of being neurally active characterizes the second or the third level of selection. One possibility is that during WM maintenance multiple representations – those in the direct-access region – are active at the same time, such that their pattern of neural activity is superimposed. Another possibility is that only one item – the one in the focus of attention – is neurally active at any time. If the focus of attention circulates among the items in WM, it would still be possible to decode several items from neural activation patterns ( Emrich, Rigall, LaRocque, & Postle, 2013 ) because the temporal resolution of decoding from BOLD signals is lower than the speed at which the focus of attention shifts from one item to another (i.e., about 300 ms; Oberauer, 2003 ).

Univariate neural correlates of WM load, most notably the amplitude of the CDA ( Vogel & Machizawa, 2004 ) and the BOLD activation in the inter-parietal sulcus (IPS) ( Todd & Marois, 2004 , 2005 ; Xu & Chun, 2006 ), imply that at least some form of persistent neural activity increases with the number of items maintained in WM. These neural measures, however, do not carry information about the content of WM, and therefore we do not know whether they reflect neurally active representations or some neural activity reflecting control processes that are involved in maintaining items selected. Another open question is whether these univariate measures of WM load reflect the first or the second level of selection – to find out we need studies that track these neural indicators of WM load while a retro-cue asks participants to select a subset of the current memory set: Does the neural marker track the set size of the subset or of the entire memory set? One study asking this question found that BOLD activation in IPS reflects the size of the entire memory set before the retro-cue but the size of the cued subset afterwards ( Lepsien, Thornton, & Nobre, 2011 ), suggesting that IPS activation reflects the second level of selection, the direct-access region. In that study, however, participants were not asked to still maintain the not-cued subset in memory, so we don’t know whether they maintained it (at the third selection level, the activated part of LTM) or just removed it from WM.

A somewhat speculative hypothesis on how to reconcile all these findings is that univariate markers of WM load track the amount of information selected at the second level (i.e., the direct-access region). This information is maintained in WM through temporary bindings between contents and contexts through which they are accessible, probably in parietal cortex. These bindings are neurally silent – either because they are implemented through rapid synaptic plasticity ( Mongillo, Barak, & Tsodyks, 2008 ) or because they are implemented in a pattern of neural activity that bears no similarity to the bound contents, such as a circular convolution of each content with its context ( Eliasmith, 2013 ; Plate, 2003 ), so that they cannot be identified through decoding of the WM contents. However, neural activity patterns corresponding to the contents of the direct-access region could be re-activated during the retention interval by feeding non-specific activation into the contexts that act as retrieval cues for these contents, so that they could (faintly) be decoded from parietal cortical areas ( Bettencourt & Xu, 2016 ; Christophel et al., 2018 ). This non-specific activation could be spontaneous noise in the neural network ( Oberauer & Lin, 2017 ), or an attentional mechanism that selectively activates all contexts to which the contents of the direct-access region are bound. The content (or contents) selected for the third level of selection, the focus of attention, is represented in a neurally active fashion, probably in the prefrontal cortex ( Bichot, Heard, DeGennaro, & Desimone, 2015 ; Mendoza-Halliday & Martinez-Trujillo, 2017 ), and this representation re-activates the corresponding sensory representation in those sensory cortical areas involved in its initial processing, so that the information in the focus of attention can be decoded from neural activity in those areas.

A prediction from this hypothesis is that when two to-be-remembered stimuli are presented sequentially, univariate markers such as the CDA should add up to reflect the combined load of both stimuli, whereas the decodability of the first stimulus should be substantially impaired by the encoding of the second, because the focus of attention abandons the first to encode the second stimulus. Evidence for the first assumption comes from studies showing that the CDA reflects the combined load of two successively presented parts of a memory set ( Feldmann-Wüstefeld, Vogel, & Awh, 2018 ; Ikkai, McCollough, & Vogel, 2010 ); the second prediction remains to be tested.

What is the Relation between WM and Perceptual Attention?

An extreme position would be that WM and perceptual attention are the same: By virtue of attending to a perceived stimulus, it is selected into WM. Maintaining stimuli in WM that are no longer present in the environment differs from perceptual attention only in the absence of the physical stimulus. The cognitive state is still the same, with the only difference that the representation in WM is arguably weaker and less precise due to the lack of informative sensory input. This extreme position is attractive due to its parsimony, but it is almost certainly wrong. We have already seen that perceptual attention to stimuli during the retention interval of a visual WM task leads to less interference than adding the same stimuli to WM ( Fougnie & Marois, 2006 ). I have also discussed instances where stimuli were attended to and yet they leave hardly any trace in WM (H. Chen et al., 2016 ; H. Chen & Wyble, 2015a , 2015b ; Oberauer, 2018 ). Moreover, single-cell recordings from monkey LPFC neurons showed partial but not complete overlap between the neurons responding selectively to a feature while it is perceptually attended and those doing so while the feature is being held in WM ( Mendoza-Halliday & Martinez-Trujillo, 2017 ). If we accept that perceptual attention and WM are different entities, we can meaningfully ask how they causally affect each other.

How does perceptual attention affect WM? Some authors have argued that perceptual attention can be used to rehearse visual or spatial WM contents. The evidence for this idea is mixed. Some studies found a correlation between spontaneous eye movements during the retention interval – which presumably track visual attention – and recall success for sequences of spatial locations ( Tremblay, Saint-Aubin, & Jalberg, 2006 ), but no such correlation was found for change detection in visual arrays ( Williams, Pouget, Boucher, & Woodman, 2013 ). Directing people to attend to individual items in a visual array improves memory for those items relative to not-attended items in the array ( Souza, Rerko, & Oberauer, 2015 ; Souza, Vergauwe, & Oberauer, 2018 ). However, it is not clear whether this effect relies on perceptual attention. Engaging perceptual attention by a secondary task during the retention interval (i.e., detection of a slight brightness change in the fixation cross) impaired performance in a visual change-detection task ( Williams et al., 2013 ), but had at best a negligible effect on errors in a visual continuous-reproduction task, whereas engaging central attention impaired continuous reproduction more severely ( Souza & Oberauer, 2017 ).

As discussed above in the section on Filtering, perceptual attention is probably necessary but not sufficient for encoding of stimuli into WM. Yet, filtering is not perfect, so that attended information is sometimes encoded into WM to some extent even when this is not desired. To the extent that this happens, we can expect that distractors presented during the retention interval of a WM task interfere with the to-be-remembered information, thereby impairing memory performance.

Evidence for such interference comes from studies of spatial WM. Van der Stigchel, Merten, Meeter, and Theeuwes ( 2007 ) found that recall of locations is biased towards the location of a suddenly appearing irrelevant stimulus on the screen, suggesting that this stimulus was inadvertently encoded into WM. Lawrence, Myerson, and Abrams ( 2004 ) had participants identify and compare two symbols during the retention interval of a WM task, which either appeared at fixation or in the periphery (left or right of fixation). When the symbols appeared in the periphery, spatial (but not verbal) WM performance was impaired more than for centrally displayed symbols. This suggests that attending to additional locations entails encoding these locations into WM to some degree, thereby interfering with memory for other locations. The interfering effect was stronger when participants were instructed to move their eyes to the peripheral symbols than when they were instructed to maintain fixation, in line with other findings showing that processing distractors enforces stronger encoding into WM than merely attending to them ( Oberauer, 2018 ). Both studies unfortunately lack a control condition in which irrelevant stimuli are presented but not attended, so it is not clear how much perceptual attention contributes to their encoding into WM.

Does attending to a stimulus in the environment distract the focus of attention from information in WM? Two observations indicate that it might not: The beneficial effect of a retro-cue directing the focus of attention to one item in WM is not diminished by a subsequent task engaging perceptual attention ( Hollingworth & Maxcey-Richard, 2013 ; Rerko, Souza, & Oberauer, 2014 ). Likewise, the object-repetition benefit in a spatial WM updating task was not diminished by requiring people to focus visual attention on a stimulus in the periphery in between updating steps ( Hedge, Oberauer, & Leonards, 2015 ). However, the retro-cue effect probably arises in part from strengthening of the cued item’s binding to its context, and this effect lasts after the focus of attention has moved away from the cued item ( Rerko et al., 2014 ; Souza et al., 2015 ). The same could be true for the object-repetition benefit: The item to be updated is selected into the focus of attention, and this strengthens the item’s binding to its context as a side effect, leaving that item temporarily more accessible than other items even if the focus of attention moves away from it. Evidence suggesting that attending to perceptual stimuli does distract the focus of attention comes from studies using multivariate neural signals to read out the information in the pattern of neural activity. The decodability of a single item in WM is drastically diminished – at least temporarily – by the onset of an irrelevant stimulus, or just by the person attending to a location in anticipation of a stimulus, during the retention interval ( Bettencourt & Xu, 2016 ; van Moorselaar et al., 2017 ). However, in these studies the irrelevant stimulus hardly affected memory performance. Therefore, an alternative possibility is that the content of the focus of attention is represented in pre-frontal cortex ( Bichot et al., 2015 ), and the corresponding sensory representations are merely epiphenomenal, so that the elimination of the latter does not imply a distraction of the focus of attention in WM.

To conclude, surprisingly little can be said with confidence: Perceptual attention to stimuli often – but not always – leads to them being encoded into WM to some extent, so that they interfere with similar information. The use of perceptual attention for rehearsal has not been demonstrated convincingly. Whether the focus of attention can stay on an item in WM while perceptual attention engages with a different stimulus in the environment is still unclear.

How does information in WM affect perceptual attention? It appears plausible that holding some information in WM tends to draw perceptual attention to similar information in the environment, thereby facilitating its processing. Initial evidence for that assumption comes from experiments by Awh et al. ( 1998 ): Holding the spatial location of an object in WM facilitates processing of other stimuli appearing in the same location during the retention interval. A subsequent similar study taking additional measures to discourage eye movements, however, failed to replicate this finding ( Belopolsky & Theeuwes, 2009 ).

A more specific version of the same idea is the assumption that the item held in the focus of attention in WM – usually a single item – functions as a “search template”, guiding perceptual attention to matching stimuli ( Olivers, Peters, Houtkamp, & Roelfsema, 2011 ). This idea has received considerable empirical support from studies of the “attentional capture” effect in visual search: When people are asked to hold an item in WM – for instance a color, or just a color word – and carry out a visual search task during the retention interval, attention is drawn to stimuli in the search display matching the item in WM ( Soto, Hodsoll, Rotshtein, & Humphreys, 2008 ). When more than one item is held in WM and one of them is retro-cued, then only the retro-cued item causes attentional capture ( Mallett & Lewis-Peacock, 2018 ; van Moorselaar, Battistoni, Theeuwes, & Olivers, 2014 ; van Moorselaar, Theeuwes, & Olivers, 2014 ). This finding provides further evidence for the special functional status of representations in the focus of attention (i.e., the third level of selection).

How is WM related to the control of attention and action?

Some theorists argue for a close relation of WM specifically to controlled attention ( Kane et al., 2001 ; McVay & Kane, 2009 ; Unsworth et al., 2014 ). The evidence for this link comes primarily from correlations between measures of WM capacity and controlled attention (reviewed above in the section on resources for attention control). There are at least two interpretations of this correlation. One is that people with high ability to control their attention are good at keeping irrelevant contents out of WM ( Hasher & Zacks, 1988 ), either by filtering them out at encoding ( Vogel et al., 2005 ) or by removing them once they are no longer relevant ( Oberauer et al., 2012 ), and therefore they make better use of their WM capacity. This account has difficulties explaining why measures of controlled attention were found to correlate substantially also with measures of (visual) WM in which no irrelevant stimuli were presented, and no contents need to be removed from WM ( Unsworth et al., 2014 ).

A second explanation, which I believe to be more promising, implies the reverse direction of causality. It starts from the assumption that the main function of WM is to hold representations that control what we think and do, including what we direct our attention to ( Oberauer, 2009 ). For instance, in visual search perceptual attention can be controlled by holding a template of the search target in the focus of attention in WM ( Olivers et al., 2011 ). Selection of responses to stimuli in accordance with the currently relevant task goal is accomplished by holding a task set – a representation of the relevant stimulus categories, the response options, and the mapping between them – in WM ( Monsell, 2003 ; Oberauer et al., 2013 ). In both cases, control could also rely on representations in long-term memory. For the case of visual search, Woodman, Carlisle, and Reinhart ( 2013 ) present strong evidence that search targets that repeat across successive trials are held in WM only for the first few trials, after which search is controlled by target representations in long-term memory. The finding that search becomes more efficient with practice when the same set of stimuli is consistently used as targets or distractors further underscores the role of long-term memory in controlling perceptual attention in search tasks ( Shiffrin & Schneider, 1977 ). For the case of response selection, practicing a task with consistent stimulus-response mappings leads to long-term learning of these mappings, greatly improving task performance. Representations in WM are necessary for control when we want to do something new – searching for a new target, or carrying out a new task that we just learned from instruction. WM representations are particularly important when the new action is inconsistent with one that we have learned – for instance, searching for a target that used to consistently figure as distractor, or switching from one task to another that maps the same stimuli to new responses. In these cases, WM provides a medium for building and maintaining new representations that control our cognitive processes and actions, if necessary countermanding our long-term knowledge. On these assumptions, the correlation between WM capacity and performance in controlled-attention tasks arises because people with better WM capacity have better (i.e., more robust, more precise) representations in WM of the (cognitive or overt) action they intend to carry out, such as search templates and task sets.

To conclude, I argue that WM plays a crucial role in controlling attention and action by holding the representations that guide attention and action. The control process consists of selecting these representations into WM – once they are established in WM, they have their influence on attention and action automatically: Perceptual attention is “captured” by stimuli matching the content of the focus of attention even when this is only detrimental to performance in the current task ( Foerster & Schneider, 2018 ; Gao et al., 2016 ); newly instructed tasks, once implemented as task sets in WM, function like a “prepared reflex”, influencing response selection even when they are currently not relevant ( Meiran, Liefooghe, & De Houwer, 2017 ).

Conclusions

Attention is closely related to WM. Unpacking this relationship reveals many different ways in which the WM-attention link can be spelled out. A first divide is between theoretical ideas about attention as a resource on the one hand, and about attention as a mechanism for selecting and prioritizing information on the other. The first approach entails the theoretical commitment that a limited attentional resource is at least in part responsible for the capacity limit of WM. This assumption has considerable empirical support but also significant weaknesses (for a review see Oberauer et al., 2016 ), so that researchers should not endorse it as a default. The second approach does not imply a commitment to any assumptions about WM or attention, and therefore offers a more neutral starting point for asking how the two are related. From the theoretical considerations and the evidence reviewed here I conclude that the following assertions about specific relations between attention and WM are justified:

• By virtue of holding a selected subset of all available representations in memory, WM is by definition a form of attention.
• The selection of information to be held in WM is a form of controlled attention: The selection of stimuli to be encoded into WM is controlled by a filtering mechanism set according to our intentions; the retrieval of long-term memory information into WM is gated to admit only information relevant for our current goals, and information no longer relevant for our current goal is removed from WM.
• Attending to a perceived stimulus probably facilitates encoding of that stimulus into WM, but does not mandate it. Even attended information can be, to a large extent, filtered out.
• Within the contents of WM the focus of attention can be directed to individual items, or subsets of items, selected for manipulating them, or as input for processes (e.g., mental arithmetic, visual search).
• Control of attention and action relies on representations in WM that guide attention and action, such as search templates and task sets, especially when these are new and in conflict with knowledge in long-term memory. Once established in WM, these representations control attention and action independently of our intentions.

Unsurprisingly, there are also many things we don’t know. Table 2 presents a non-exhaustive list of open questions that I believe future research should address with high priority. I hope that this effort will lead to an increasingly more precise and nuanced picture of how WM is related to attention.

Open Questions.

I will use the term object (of attention) in a broad sense, referring to every entity that we can pay attention to (e.g., physical objects, events, people, concepts and ideas, goals and actions, …).  

Chun et al. ( 2011 ) refer to this distinction as “internal” vs. “external” attention. I find this terminology misleading: The memory of a tree is not more internal than the perception of a tree: Both are internal representations of external objects.  

Another paradoxical implication of the fusion account is that, once the resource is completely absorbed for storage purposes, there is no resource left for control processes clearing irrelevant material from WM, and once an ongoing process monopolizes the entire attentional resource, there is no way of stopping it. A meta-control process is necessary to ensure that there is always enough resource left for control processes. If the meta-control process needs a share of the resource for itself, we are on the way to an infinite regress.  

The term “unattended” is to be understood relative to the “attended” content of WM. At the same time, all contents of WM are prioritized over all other memory representations, and as such are attended, though on a broader level of selection.  

Ethics and Consent

This article reports no original research, so no ethics approval is required.

Funding Information

The work on this article was supported by a grant from the Swiss National Science Foundation (SNSF, grant number 100014_135002). Thanks to Peter Shepherdson and Claudia von Bastian for their comments on a previous version of this manuscript.

Competing Interests

The author has no competing interests to declare.

Allen, R. J., Baddeley, A. D., & Hitch, G. J. (2006). Is the binding of visual features in working memory resource-demanding? Journal of Experimental Psychology: General , 135, 298–313. DOI: https://doi.org/10.1037/0096-3445.135.2.298  

Anderson, J. R., Reder, L. M., & Lebiere, C. (1996). Working memory: Activation limits on retrieval. Cognitive Psychology , 30, 221–256. DOI: https://doi.org/10.1006/cogp.1996.0007  

Awh, E., Belopolsky, A. V., & Theeuwes, J. (2012). Top-down versus bottom-up attentional control: a failed theoretical dichotomy. Trends in Cognitive Sciences , 16, 437–443. DOI: https://doi.org/10.1016/j.tics.2012.06.010  

Awh, E., Jonides, J., & Reuter-Lorenz, P. A. (1998). Rehearsal in spatial working memory. Journal of Experimental Psychology: Human Perception and Performance , 24, 780–790. DOI: https://doi.org/10.1037/0096-1523.24.3.780  

Baddeley, A. D. (1986). Working memory . Oxford: Clarendon Press.  

Baddeley, A. D. (1993). Working memory or working attention? In: A. Baddeley, & L. Weiskrantz (Eds.), Attention: Selection, awareness, and control , (pp. 152–170). Oxford: Clarendon.  

Baddeley, A. D. (1996). Exploring the central executive. Quarterly Journal of Experimental Psychology , 49A, 5–28. DOI: https://doi.org/10.1080/713755608  

Baddeley, A. D., Papagno, C., & Andrade, J. (1993). The sandwich effect: The role of attentional factors in serial recall. Journal of Experimental Psychology: Learning, Memory and Cognition , 19, 862–870. DOI: https://doi.org/10.1037/0278-7393.19.4.862  

Barrouillet, P., Bernardin, S., & Camos, V. (2004). Time constraints and resource sharing in adults’ working memory spans. Journal of Experimental Psychology: General , 133, 83–100. DOI: https://doi.org/10.1037/0096-3445.133.1.83  

Barrouillet, P., Bernardin, S., Portrat, S., Vergauwe, E., & Camos, V. (2007). Time and cognitive load in working memory. Journal of Experimental Psychology: Learning, Memory & Cognition , 33, 570–585. DOI: https://doi.org/10.1037/0278-7393.33.3.570  

Barrouillet, P., Portrat, S., & Camos, V. (2011). On the law relating processing to storage in working memory. Psychological Review , 118, 175–192. DOI: https://doi.org/10.1037/a0022324  

Belopolsky, A. V., & Theeuwes, J. (2009). No functional role of attention-based rehearsal in maintenance of spatial working memory representations. Acta Psychologica , 132, 124–135. DOI: https://doi.org/10.1016/j.actpsy.2009.01.002  

Bettencourt, K. C., & Xu, Y. (2016). Decoding the content of visual short-term memory under distraction in occipital and parietal areas. Nature Neuroscience , 19, 150–157. DOI: https://doi.org/10.1038/nn.4174  

Bichot, N. P., Heard, M. T., DeGennaro, E. M., & Desimone, R. (2015). A Source for Feature-Based Attention in the Prefrontal Cortex. Neuron , 88(4), 832–844. DOI: https://doi.org/10.1016/j.neuron.2015.10.001  

Bunting, M. F., Cowan, N., & Colflesh, G. J. H. (2008). The deployment of attention in short-term memory tasks: Trade-offs between immediate and delayed deployment. Memory & Cognition , 36, 799–812. DOI: https://doi.org/10.3758/MC.36.4.799  

Camos, V., Lagner, P., & Barrouillet, P. (2009). Two maintenance mechanisms of verbal information in working memory. Journal of Memory and Language , 61, 457–469. DOI: https://doi.org/10.1016/j.jml.2009.06.002  

Case, R. (1972). Validation of a neo-Piagetian mental capacity construct. Journal of Experimental Child Psychology , 14, 287–302. DOI: https://doi.org/10.1016/0022-0965(72)90051-3  

Case, R., Kurland, M., & Goldberg, J. (1982). Operational efficiency and the growth of short-term memory span. Journal of Experimental Child Psychology , 33, 386–404. DOI: https://doi.org/10.1016/0022-0965(82)90054-6  

Chein, J. M., Moore, A. B., & Conway, A. R. A. (2011). Domain-general mechanisms of complex working memory span. NeuroImage , 54, 550–559. DOI: https://doi.org/10.1016/j.neuroimage.2010.07.067  

Chen, H., Swan, G., & Wyble, B. (2016). Prolonged focal attention without binding: Trackng a ball for half a minute without remembering its color. Cognition , 147, 144–148. DOI: https://doi.org/10.1016/j.cognition.2015.11.014  

Chen, H., & Wyble, B. (2015a). Amnesia for object attributes: Failure to report attended information that had just reached conscious awareness. Psychological Science , 26, 203–210. DOI: https://doi.org/10.1177/0956797614560648  

Chen, H., & Wyble, B. (2015b). The location but not the attributes of visual cues are automatically encoded into working memory. Vision Research , 107, 76–85. DOI: https://doi.org/10.1016/j.visres.2014.11.010  

Chen, Z., & Cowan, N. (2009). How verbal memory loads consume attention. Memory & Cognition , 37, 829–836. DOI: https://doi.org/10.3758/MC.37.6.829  

Christophel, T. B., Iamshchinina, P., Yan, C., Allefeld, C., & Haynes, J.-D. (2018). Cortical specialization for attended versus unattended working memory. Nature Neuroscience , 21, 494–496. DOI: https://doi.org/10.1038/s41593-018-0094-4  

Chuderski, A. (2014). The relational integration task explains fluid reasoning above and beyond other working memory tasks. Memory & Cognition , 42, 448–463. DOI: https://doi.org/10.3758/s13421-013-0366-x  

Chun, M. M. (2011). Visual working memory as visual attention sustained internally over time. Neuropsychologia , 49(6), 1407–1409. DOI: https://doi.org/10.1016/j.neuropsychologia.2011.01.029  

Chun, M. M., Golomb, J. D., & Turk-Browne, N. B. (2011). A taxonomy of external and internal attention. Annual Review of Psychology , 62, 73–101. DOI: https://doi.org/10.1146/annurev.psych.093008.100427  

Cowan, N. (1995). Attention and memory: An integrated framework . New York: Oxford University Press.  

Cowan, N. (2001). The magical number 4 in short-term memory: A reconsideration of mental storage capacity. Behavioral and Brain Sciences , 24, 87–185. DOI: https://doi.org/10.1017/S0140525X01003922  

Cowan, N. (2005). Working memory capacity . New York: Psychology Press.  

Cowan, N. (2017). The many faces of working memory and short-term storage. Psychonomic Bulletin & Review , 24, 1158–1170. DOI: https://doi.org/10.3758/s13423-016-1191-6  

Cowan, N., Elliott, E. M., Saults, J. S., Morey, C. C., Mattox, S., Hismjatullina, A., & Conway, A. R. A. (2005). On the capacity of attention: its estimation and its role in working memory and cognitive aptitudes. Cognitive Psychology , 51, 42–100. DOI: https://doi.org/10.1016/j.cogpsych.2004.12.001  

Cowan, N., Fristoe, N., Elliott, E. M., Brunner, R. P., & Saults, J. S. (2006). Scope of attention, control of attention, and intelligence in children and adults. Memory & Cognition , 34, 1754–1768. DOI: https://doi.org/10.3758/BF03195936  

Daneman, M., & Carpenter, P. A. (1980). Individual differences in working memory and reading. Journal of Verbal Learning and Verbal Behavior , 19, 450–466. DOI: https://doi.org/10.1016/S0022-5371(80)90312-6  

Desimone, R., & Duncan, J. (1995). Neural mechanisms of selective visual attention. Annual Review of Neuroscience , 18, 193–222. DOI: https://doi.org/10.1146/annurev.ne.18.030195.001205  

Ecker, U. K. H., Lewandowsky, S., & Oberauer, K. (2014). Removal of information from working memory: A specific updating process. Journal of Memory and Language , 74, 77–90. DOI: https://doi.org/10.1016/j.jml.2013.09.003  

Ecker, U. K. H., Oberauer, K., & Lewandowsky, S. (2014). Working memory updating involves item-specific removal. Journal of Memory & Language , 74, 1–15. DOI: https://doi.org/10.1016/j.jml.2014.03.006  

Eliasmith, C. (2013). How to build a brain: A neural architecture for biological cognition . New York, NY: Oxford University Press. DOI: https://doi.org/10.1093/acprof:oso/9780199794546.001.0001  

Emrich, S. M., Rigall, A. C., LaRocque, J. J., & Postle, B. R. (2013). Distributed patterns of activity in sensory cortex reflect the precision of multiple items maintained in visual short-term memory. Journal of Neuroscience , 33, 6516–6523. DOI: https://doi.org/10.1523/JNEUROSCI.5732-12.2013  

Ester, E. F., Fukuda, K., May, L. M., Vogel, E. K., & Awh, E. (2014). Evidence for a fixed capacity limit in attending multiple locations. Cognitive, Affective, & Behavioral Neuroscience , 14, 62–77. DOI: https://doi.org/10.3758/s13415-013-0222-2  

Feldmann-Wüstefeld, T., Vogel, E. K., & Awh, E. (2018). Contralateral delay activity indexes working memory storage, not the current focus of spatial attention. Journal of Cognitive Neuroscience , 30(8), 1185–1196. DOI: https://doi.org/10.1162/jocn_a_01271  

Foerster, R. M., & Schneider, W. X. (2018). Involuntary top-down control by search-irrelevant features: Visual working memory biases attention in an object-based manner. Cognition , 172, 37–45. DOI: https://doi.org/10.1016/j.cognition.2017.12.002  

Fougnie, D., & Marois, R. (2006). Distinct capacity limits for attention and working memory. Psychological Science , 17, 526–534. DOI: https://doi.org/10.1111/j.1467-9280.2006.01739.x  

Gao, Z., Yu, S., Zhu, C., Shui, R., Weng, X., Li, P., & Shen, M. (2016). Object-based encoding in visual working memory: Evidence from memory-driven attentional capture. Scientific Reports , 6. DOI: https://doi.org/10.1038/srep22822  

Garavan, H. (1998). Serial attention within working memory. Memory & Cognition , 26, 263–276. DOI: https://doi.org/10.3758/BF03201138  

Gazzaley, A., & Nobre, A. C. (2012). Top-down modulation: bridging selective attention and working memory. Trends in Cognitive Sciences , 16, 129–135. DOI: https://doi.org/10.1016/j.tics.2011.11.014  

Gilchrist, A. L., & Cowan, N. (2011). Can the focus of attention accommodate multiple, separate items? Journal of Experimental Psychology: Learning, Memory, and Cognition , 37, 1484–1502. DOI: https://doi.org/10.1037/a0024352  

Göthe, K., Oberauer, K., & Kliegl, R. (2016). Eliminating dual-task costs by minimizing crosstalk between tasks: The role of modality and feature pairings. Cognition , 150, 92–108. DOI: https://doi.org/10.1016/j.cognition.2016.02.003  

Griffin, I. C., & Nobre, A. C. (2003). Orienting attention to locations in internal representations. Journal of Cognitive Neuroscience , 15, 1176–1194. DOI: https://doi.org/10.1162/089892903322598139  

Hasher, L., & Zacks, R. T. (1988). Working memory, comprehension, and aging: A review and a new view. In: G. H. Bower (Ed.), The Psychology of Learning and Motivation , 22, (pp. 193–225). New York: Academic Press. DOI: https://doi.org/10.1016/S0079-7421(08)60041-9  

Hasher, L., Zacks, R. T., & May, C. P. (1999). Inhibitory control, circadian arousal, and age. In: D. Gopher, & A. Koriat (Eds.), Attention and Performance , (pp. 653–675). Cambridge, MA: MIT Press.  

Hazeltine, E., & Witfall, T. (2011). Searching working memory for the source of dual-task costs. Psychological Research , 75, 466–475. DOI: https://doi.org/10.1007/s00426-011-0343-6  

Hedge, C., Oberauer, K., & Leonards, U. (2015). Selection in spatial working memory is independent of perceptual selective attention, but they interact in a shared spatial priority map. Attention, Perception & Psychophysics , 77, 26653–22668. DOI: https://doi.org/10.3758/s13414-015-0976-4  

Hollingworth, A., & Beck, V. M. (2016). Memory-based attention capture when multiple items are maintained in visual working memory. Journal of Experimental Psychology: Human Perception and Performance , 42, 911–917. DOI: https://doi.org/10.1037/xhp0000230  

Hollingworth, A., & Maxcey-Richard, A. M. (2013). Selective maintenance in visual working memory does not require sustained visual attention. Journal of Experimental Psychology: Human Perception and Performance , 39, 1047–1058. DOI: https://doi.org/10.1037/a0030238  

Ikkai, A., McCollough, A. W., & Vogel, E. K. (2010). Contralateral delay activity provides a neural measure of the number of representations in visual working memory. Journal of Neurophysiology , 103, 1963–1968. DOI: https://doi.org/10.1152/jn.00978.2009  

Jolicoeur, P., & Dell’Acqua, R. (1998). The demonstration of short-term consolidation. Cognitive Psychology , 36, 138–202. DOI: https://doi.org/10.1006/cogp.1998.0684  

Jurado, M. B., & Rosselli, M. (2007). The elusive nature of executive functions: A review of our current understanding. Neuropsychology Review , 17, 213–233. DOI: https://doi.org/10.1007/s11065-007-9040-z  

Just, M. A., & Carpenter, P. A. (1980). A theory of reading: From eye fixations to comprehension. Psychological Review , 87, 329–354. DOI: https://doi.org/10.1037/0033-295X.87.4.329  

Just, M. A., & Carpenter, P. A. (1992). A capacity theory of comprehension: Individual differences in working memory. Psychological Review , 99, 122–149. DOI: https://doi.org/10.1037/0033-295X.99.1.122  

Kane, M. J., Bleckley, M. K., Conway, A. R. A., & Engle, R. W. (2001). A controlled-attention view of working-memory capacity. Journal of Experimental Psychology: General , 130, 169–183. DOI: https://doi.org/10.1037/0096-3445.130.2.169  

Kane, M. J., & Engle, R. W. (2003). Working-memory capacity and the control of attention: The contributions of goal neglect, response competition, and task set to Stroop interference. Journal of Experimental Psychology: General , 132(47–70). DOI: https://doi.org/10.1037/0096-3445.132.1.47  

Kelley, T. A., & Lavie, N. (2011). Working Memory Load Modulates Distractor Competition in Primary Visual Cortex. Cerebral Cortex , 21(3), 659–665. DOI: https://doi.org/10.1093/cercor/bhq139  

Keye, D., Wilhelm, O., Oberauer, K., & van Ravenzwaaij, D. (2009). Individual differences in conflict-monitoring: Testing means and covariance hypothesis about the Simon and the Eriksen Flanker task. Psychological Research-Psychologische Forschung , 73(6), 762–776. DOI: https://doi.org/10.1007/s00426-008-0188-9  

Kim, S.-Y., Kim, M.-S., & Chun, M. M. (2005). Concurrent working memory load can reduce distraction. Proceedings of the National Academy of Sciences , 102, 16524–16529. DOI: https://doi.org/10.1073/pnas.0505454102  

Kiyonaga, A., & Egner, T. (2014). Working memory as internal attention: Toward an integrative account of internal and external selection processes. Psychonomic Bulletin & Review . DOI: https://doi.org/10.3758/s13423-012-0359-y  

Klapp, S. T., Marshburn, E. A., & Lester, P. T. (1983). Short-term memory does not involve the “working memory” of information processing: The demise of a common assumption. Journal of Experimental Psychology: General , 112, 240–264. DOI: https://doi.org/10.1037/0096-3445.112.2.240  

Konstantinou, N., Beal, E., King, J.-R., & Lavie, N. (2014). Working memory load and distraction: Dissociable effects of visual maintenance and cognitive control. Attention, Perception & Psychophysics , 76, 1985–1997. DOI: https://doi.org/10.3758/s13414-014-0742-z  

Konstantinou, N., & Lavie, N. (2013). Dissociable roles of different types of working memory load in visual detection. Journal of Experimental Psychology: Human Perception and Performance , 39, 919–924. DOI: https://doi.org/10.1037/a0033037  

Landman, R., Spekreijse, H., & Lamme, V. A. F. (2003). Large capacity storage of integrated objects before change blindness. Vision Research , 43(149–164). DOI: https://doi.org/10.1016/S0042-6989(02)00402-9  

LaRocque, J. J., Lewis-Peacock, J. A., Drysdale, A. T., Oberauer, K., & Postle, B. R. (2013). Decoding attended information in short-term memory: An EEG study. Journal of Cognitive Neuroscience , 25(1), 127–142. DOI: https://doi.org/10.1162/jocn_a_00305  

Lavie, N. (2005). Distracted and confused?: Selective attention under load. Trends in Cognitive Sciences , 9, 75–82. DOI: https://doi.org/10.1016/j.tics.2004.12.004  

Lavie, N., Hirst, A., de Fockert, J. W., & Viding, E. (2004). Load theory of selective attention and cognitive control. Journal of Experimental Psychology: General , 133, 339–354. DOI: https://doi.org/10.1037/0096-3445.133.3.339  

Lawrence, B. M., Myerson, J., & Abrams, R. A. (2004). Interference with spatial working memory: An eye movement is more than a shift of attention. Psychonomic Bulletin & Review , 11, 488–494. DOI: https://doi.org/10.3758/BF03196600  

Lawrence, B. M., Myerson, J., Oonk, H. M., & Abrams, R. A. (2001). The effects of eye and limb movements on working memory. Memory , 9, 433–444. DOI: https://doi.org/10.1080/09658210143000047  

Lepsien, J., Thornton, I., & Nobre, A. C. (2011). Modulation of working-memory maintenance by directed attention. Neuropsychologia , 49, 1569–1577. DOI: https://doi.org/10.1016/j.neuropsychologia.2011.03.011  

Lewis-Peacock, J. A., Drysdale, A. T., Oberauer, K., & Postle, B. R. (2011). Neural evidence for a distinction between short-term memory and the focus of attention. Journal of Cognitive Neuroscience , 24, 61–79. DOI: https://doi.org/10.1162/jocn_a_00140  

Lewis-Peacock, J. A., Kessler, Y., & Oberauer, K. (2018). The removal of information from working memory. Annals of the New York Academy of Science , 1424, 33–44. DOI: https://doi.org/10.1111/nyas.13714  

Liefooghe, B., Barrouillet, P., Vandierendonck, A., & Camos, V. (2008). Working memory costs of task switching. Journal of Experimental Psychology: Learning, Memory, and Cognition , 34, 478–494. DOI: https://doi.org/10.1037/0278-7393.34.3.478  

Logie, R. H., Brockmole, J. B., & Jaswal, S. (2011). Feature binding in visual short-term memory is unaffected by task-irrelevant changes of location, shape, and color. Memory & Cognition , 39, 24–36. DOI: https://doi.org/10.3758/s13421-010-0001-z  

Luria, R., Balaban, H., Awh, E., & Vogel, E. K. (2016). The contralateral delay activity as a neural measure of visual working memory. Neuroscience and Biobehavioral Reviews , 62, 100–108. DOI: https://doi.org/10.1016/j.neubiorev.2016.01.003  

Ma, W. J., Husain, M., & Bays, P. M. (2014). Changing concepts of working memory. Nature Neuroscience Reviews , 17, 347–356. DOI: https://doi.org/10.1038/nn.3655  

Mall, J. T., Morey, C. C., Wolff, M. J., & Lehnert, F. (2014). Visual selective attention is equally functional for individuals with low and high working memory capacity: Evidence from accuracy and eye movements. Attention, Perception & Psychophysics . DOI: https://doi.org/10.3758/s13414-013-0610-2  

Mallett, R., & Lewis-Peacock, J. A. (2018). Behavioral decoding of working memory items inside and outside the focus of attention. Annals of the New York Academy of Sciences , 1424(1), 256–267. DOI: https://doi.org/10.1111/nyas.13647  

Marshall, L., & Bays, P. M. (2013). Obligatory encoding of task-irrelevant features depletes working memory resources. Journal of Vision , 13, 1–13. DOI: https://doi.org/10.1167/13.2.21  

McElree, B. (2006). Accessing recent events. In: B. H. Ross (Ed.), The Psychology of Learning and Motivation , 46, (pp. 155–200). San Diego: Academic Press. DOI: https://doi.org/10.1016/S0079-7421(06)46005-9  

McVay, J. C., & Kane, M. J. (2009). Conducting the train of thought: Working memory capacity, goal neglect, and mind wandering in an executive-control task. Journal of Experimental Psychology: Learning, Memory, and Cognition , 35, 196–204. DOI: https://doi.org/10.1037/a0014104  

McVay, J. C., & Kane, M. J. (2012). Why does working memory capacity predict variation in reading comprehension? On the influence of mind wandering and executive attention. Journal of Experimental Psychology: General , 141, 302–320. DOI: https://doi.org/10.1037/a0025250  

Meiran, N., Liefooghe, B., & De Houwer, J. (2017). Powerful instructions: Automaticity without practice. Current Directions in Psychological Science , 26, 509–514. DOI: https://doi.org/10.1177/0963721417711638  

Mendoza-Halliday, D., & Martinez-Trujillo, J. C. (2017). Neuronal population coding of perceived and memorized visual features in the lateral prefrontal cortex. nature Communications , 8, 15471. DOI: https://doi.org/10.1038/ncomms15471  

Mongillo, G., Barak, O., & Tsodyks, M. (2008). Synaptic theory of working memory. Science , 319, 1543–1546. DOI: https://doi.org/10.1126/science.1150769  

Monsell, S. (2003). Task switching. Trends in Cognitive Sciences , 7, 134–140. DOI: https://doi.org/10.1016/S1364-6613(03)00028-7  

Morey, C. C., & Bieler, M. (2012). Visual short-term memory always requires general attention. Psychonomic Bulletin & Review , 20, 163–170. DOI: https://doi.org/10.3758/s13423-012-0313-z  

Navon, D., & Gopher, D. (1979). On the economy of the human-processing system. Psychological Review , 86, 214–255. DOI: https://doi.org/10.1037/0033-295X.86.3.214  

Navon, D., & Miller, J. (2002). Queuing or sharing? A critical evaluation of the single-bottleneck notion. Cognitive Psychology , 44, 193–251. DOI: https://doi.org/10.1006/cogp.2001.0767  

Nieuwenstein, M., & Wyble, B. (2014). Beyond a mask and against the bottleneck: Retroactive dual-task interference during working memory consolidation of a masked visual target. Journal of Experimental Psychology: General , 143, 1409–1427. DOI: https://doi.org/10.1037/a0035257  

Oberauer, K. (2001). Removing irrelevant information from working memory: A cognitive aging study with the modified Sternberg task. Journal of Experimental Psychology-Learning Memory and Cognition , 27(4), 948–957. DOI: https://doi.org/10.1037/0278-7393.27.4.948  

Oberauer, K. (2002). Access to information in working memory: Exploring the focus of attention. Journal of Experimental Psychology: Learning, Memory, and Cognition , 28(3), 411–421. DOI: https://doi.org/10.1037/0278-7393.28.3.411  

Oberauer, K. (2003). Selective attention to elements in working memory. Experimental Psychology , 50(4), 257–269. DOI: https://doi.org/10.1026//1618-3169.50.4.257  

Oberauer, K. (2005). Control of the contents of working memory – A comparison of two paradigms and two age groups. Journal of Experimental Psychology: Learning, Memory, and Cognition , 31(4), 714–728. DOI: https://doi.org/10.1037/0278-7393.31.4.714  

Oberauer, K. (2009). Design for a working memory. Psychology of Learning and Motivation: Advances in Research and Theory , 51, 45–100. DOI: https://doi.org/10.1016/S0079-7421(09)51002-X  

Oberauer, K. (2013). The focus of attention in working memory – from metaphors to mechanisms. frontiers in human neuroscience , 7. DOI: https://doi.org/10.3389/fnhum.2013.00673  

Oberauer, K. (2018). Removal of irrelevant information from working memory: Sometimes fast, sometimes slow, and sometimes not at all. Annals of the New York Academy of Science , 1424, 239–255. DOI: https://doi.org/10.1111/nyas.13603  

Oberauer, K., Awh, E., & Sutterer, D. W. (2017). The role of long-term memory in a test of visual working memory: Proactive facilitation but no proactive interference. Journal of Experimental Psychology: Learning, Memory and Cognition , 43, 1–22. DOI: https://doi.org/10.1037/xlm0000302  

Oberauer, K., & Bialkova, S. (2011). Serial and parallel processes in working memory after practice. Journal of Experimental Psychology: Human Perception and Performance , 37(2), 606–614. DOI: https://doi.org/10.1037/a0020986  

Oberauer, K., Demmrich, A., Mayr, U., & Kliegl, R. (2001). Dissociating retention and access in working memory: An age-comparative study of mental arithmetic. Memory & Cognition , 29(1), 18–33. DOI: https://doi.org/10.3758/BF03195737  

Oberauer, K., Farrell, S., Jarrold, C., & Lewandowsky, S. (2016). What limits working memory capacity? Psychological Bulletin , 142, 758–799. DOI: https://doi.org/10.1037/bul0000046  

Oberauer, K., & Hein, L. (2012). Attention to information in working memory. Current Directions in Psychological Science , 21, 164–169. DOI: https://doi.org/10.1177/0963721412444727  

Oberauer, K., & Lewandowsky, S. (2013). Evidence against decay in verbal working memory. Journal of Experimental Psychology: General , 142, 380–411. DOI: https://doi.org/10.1037/a0029588  

Oberauer, K., & Lewandowsky, S. (2014). Further evidence against decay in working memory. Journal of Memory and Language , 73, 15–30. DOI: https://doi.org/10.1016/j.jml.2014.02.003  

Oberauer, K., Lewandowsky, S., Farrell, S., Jarrold, C., & Greaves, M. (2012). Modeling working memory: An interference model of complex span. Psychonomic Bulletin & Review , 19, 779–819. DOI: https://doi.org/10.3758/s13423-012-0272-4  

Oberauer, K., & Lin, H.-Y. (2017). An interference model of visual working memory. Psychological Review , 124, 21–59. DOI: https://doi.org/10.1037/rev0000044  

Oberauer, K., Souza, A. S., Druey, M., & Gade, M. (2013). Analogous mechanisms of selection and updating in declarative and procedural working memory: Experiments and a computational model. Cognitive Psychology , 66, 157–211. DOI: https://doi.org/10.1016/j.cogpsych.2012.11.001  

Olivers, C. N. L. (2008). Interactions between visual working memory and visual attention. Frontiers in Bioscience , 13, 1182–1191. DOI: https://doi.org/10.2741/2754  

Olivers, C. N. L., Peters, J., Houtkamp, R., & Roelfsema, P. R. (2011). Different states in visual working memory: When it guides attention and when it does not. Trends in Cognitive Sciences , 15, 327–334. DOI: https://doi.org/10.1016/j.tics.2011.05.004  

Park, S., Kim, M.-S., & Chun, M. M. (2007). Concurrent working memory load can facilitate selective attention: Evidence for specialized load. Journal of Experimental Psychology: Human Perception and Performance , 33(1062–1075). DOI: https://doi.org/10.1037/0096-1523.33.5.1062  

Pashler, H. (1994). Dual-task interference in simple tasks: Data and theory. Psychological Bulletin , 116, 220–244. DOI: https://doi.org/10.1037/0033-2909.116.2.220  

Plate, T. A. (2003). Convolution-based memory models. In: L. Nadel (Ed.), Encyclopedia of cognitive science , (pp. 824–828). London: Nature Publishing Group.  

Ralph, A., Walters, J. N., Stevens, A., Fitzgerald, K. J., Tehan, G., Surprenant, A. M., Turcotte, J., et al. (2011). Immunity to proactive interference is not a property of the focus of attention in working memory. Memory & Cognition , 39, 217–230. DOI: https://doi.org/10.3758/s13421-010-0030-7  

Randall, J. G., Oswald, F. L., & Beier, M. E. (2014). Mind-wandering, cognition, and performance: A theory-driven meta-analysis of attention regulation. Psychological Bulletin , 140, 1411–1431. DOI: https://doi.org/10.1037/a0037428  

Rerko, L., Souza, A. S., & Oberauer, K. (2014). Retro-cue benefits in working memory without sustained attention. Memory & Cognition , 42, 712–728. DOI: https://doi.org/10.3758/s13421-013-0392-8  

Ricker, T. J., & Hardman, K. O. (2017). The nature of short-term consolidation in visual working memory. Journal of Experimental Psychology: General . DOI: https://doi.org/10.1037/xge0000346  

Rose, N. S., LaRocque, J. J., Riggall, A. C., Gosseries, O., Starrett, M. J., Meyering, E. E., & Postle, B. R. (2016). Reactivation of latent working memories with transcranial magnetic stimulation. Science , 354, 1136–1139. DOI: https://doi.org/10.1126/science.aah7011  

Sander, M. C., Werkle-Bergner, M., & Lindenberger, U. (2011). Binding and strategic selection in working memory: A lifespan dissociation. Psychology and Aging , 26, 612–624. DOI: https://doi.org/10.1037/a0023055  

Saults, J. S., & Cowan, N. (2007). A central capacity limit to the simultaneous storage of visual and auditory arrays in working memory. Journal of Experimental Psychology: General , 136, 663–684. DOI: https://doi.org/10.1037/0096-3445.136.4.663  

Schneider, W., & Shiffrin, R. M. (1977). Controlled and automatic human information processing: I. Detection, search, and attention. Psychological Review , 84, 1–66. DOI: https://doi.org/10.1037/0033-295X.84.1.1  

Shiffrin, R. M., & Schneider, W. (1977). Controlled and automatic human information processing: II. Perceptual learning, automatic attending, and a general theory. Psychological Review , 84, 127–190. DOI: https://doi.org/10.1037/0033-295X.84.2.127  

Shipstead, Z., Lindsey, D. R. B., Marshall, R. L., & Engle, R. E. (2014). The mechanisms of working memory capacity: Primary memory, secondary memory, and attention control. Journal of Memory and Language , 72, 116–141. DOI: https://doi.org/10.1016/j.jml.2014.01.004  

Singh, K. A., Gignac, G. E., Brydges, C. R., & Ecker, U. K. H. (2018). Working memory capacity mediates the relationship between removal and fluid intelligence. Journal of Memory and Language , 101, 18–36. DOI: https://doi.org/10.1016/j.jml.2018.03.002  

Soto, D., Hodsoll, J., Rotshtein, P., & Humphreys, G. W. (2008). Automatic guidance of attention from working memory. Trends in Cognitive Sciences , 12, 342–348. DOI: https://doi.org/10.1016/j.tics.2008.05.007  

Souza, A. S., & Oberauer, K. (2016). In search of the focus of attention in working memory: 13 years of the retro-cue effect. Attention, Perception & Psychophysics . DOI: https://doi.org/10.3758/s13414-016-1108-5  

Souza, A. S., & Oberauer, K. (2017). The contributions of visual and central attention to visual working memory. Attention, Perception & Psychophysics , 79, 1897–1916. DOI: https://doi.org/10.3758/s13414-017-1357-y  

Souza, A. S., Rerko, L., & Oberauer, K. (2015). Refreshing memory traces: Thinking of an item improves retrieval from visual working memory. Annals of the New York Academy of Sciences , 1339, 20–31. DOI: https://doi.org/10.1111/nyas.12603  

Souza, A. S., Vergauwe, E., & Oberauer, K. (2018). Where to attend next: Guiding refreshing of visual, spatial, and verbal representations in working memory. Annals of the New York Academy of Science . DOI: https://doi.org/10.1111/nyas.13621  

Sprague, T. C., Ester, E. F., & Serences, J. T. (2016). Restoring latent visual working memory representations in human cortex. Neuron , 91, 694–707. DOI: https://doi.org/10.1016/j.neuron.2016.07.006  

Szmalec, A., Vandierendonck, A., & Kemps, E. (2005). Response selection involves executive control: Evidence from the selective interference paradigm. Memory & Cognition , 33, 531–541. DOI: https://doi.org/10.3758/BF03193069  

Tehan, G., & Humphreys, M. S. (1998). Creating proactive interference in immediate recall: Building a DOG from a DART, a MOP, and a FIG. Memory & Cognition , 26, 477–489. DOI: https://doi.org/10.3758/BF03201157  

Thalmann, M., Souza, A. S., & Oberauer, K. (2019). Revisiting the attentional demands of rehearsal in working-memory tasks. Journal of Memory and Language , 105, 1–18. DOI: https://doi.org/10.1016/j.jml.2018.10.005  

Theeuwes, J. (2018). Visual selection: Usually fast and automatic; seldom slow and volitional. Journal of Cognition , 1, 1–15. DOI: https://doi.org/10.5334/joc.13  

Todd, J. J., & Marois, R. (2004). Capacity limit of visual short-term memory in human posterior parietal cortex. Nature , 428, 751–753. DOI: https://doi.org/10.1038/nature02466  

Todd, J. J., & Marois, R. (2005). Posterior parietal cortex activity predicts individual differences in visual short-term memory capacity. Cognitive, Affective, & Behavioral Neuroscience , 5, 144–155. DOI: https://doi.org/10.3758/CABN.5.2.144  

Tombu, M., & Jolicoeur, P. (2003). A central capacity sharing model of dual-task performance. Journal of Experimental Psychology: Human Perception and Performance , 29, 3–18. DOI: https://doi.org/10.1037/0096–1523.29.1.3  

Tremblay, S., Saint-Aubin, J., & Jalberg, A. (2006). Rehearsal in serial memory for visual-spatial information: Evidence from eye movements. Psychonomic Bulletin & Review , 13, 452–457. DOI: https://doi.org/10.3758/BF03193869  

Tsubomi, H., Fukuda, K., Watanabe, K., & Vogel, E. K. (2013). Neural limits to representing objects still within view. Journal of Neuroscience , 33, 8257–8263. DOI: https://doi.org/10.1523/JNEUROSCI.5348-12.2013  

Ueno, T., Allen, R. J., Baddeley, A. D., Hitch, G. J., & Saito, S. (2011). Disruption of visual feature binding in working meemory. Memory & Cognition , 39, 12–23. DOI: https://doi.org/10.3758/s13421-010-0013-8  

Unsworth, N. (2015). Consistency of attentional control as an important cognitive trait: A latent variable analysis. Intelligence , 49, 110–128. DOI: https://doi.org/10.1016/j.intell.2015.01.005  

Unsworth, N., Fukuda, K., Awh, E., & Vogel, E. K. (2014). Working memory and fluid intelligence: Capacity, attention control, and secondary memory retrieval. Cognitive Psychology , 71, 1–26. DOI: https://doi.org/10.1016/j.cogpsych.2014.01.003  

Van der Stigchel, S., Merten, H., Meeter, M., & Theeuwes, J. (2007). The effects of a task-irrelevant visual event on spatial working memory. Psychonomic Bulletin & Review , 14, 1066–1071. DOI: https://doi.org/10.3758/BF03193092  

van Moorselaar, D., Battistoni, E., Theeuwes, J., & Olivers, C. N. L. (2014). Rapid influences of cued visual memories on attentional guidance. Annals of the New York Academy of Sciences . DOI: https://doi.org/10.1111/nyas.12574  

van Moorselaar, D., Foster, J. J., Sutterer, D. W., Theeuwes, J., Olivers, C. N. L., & Awh, E. (2017). Spatially selective alpha oscillations reveal moment-by-moment trade-offs between working memory and attention. Journal of Cognitive Neuroscience , 30, 256–266. DOI: https://doi.org/10.1162/jocn_a_01198  

van Moorselaar, D., Theeuwes, J., & Olivers, C. N. L. (2014). In competition for the attentional template: Can multiple items within visual working memory guide attention? Journal of Experimental Psychology: Human Perception and Performance , 40, 1450–1464. DOI: https://doi.org/10.1037/a0036229  

Vergauwe, E., Barrouillet, P., & Camos, V. (2010). Do mental processes share a domain-general resource? Psychological Science , 21, 384–390. DOI: https://doi.org/10.1177/0956797610361340  

Vergauwe, E., Camos, V., & Barrouillet, P. (2014). The impact of storage on processing: How is information maintained in working memory? Journal of Experimental Psychology: Learning, Memory, and Cognition , 40, 1072–1095. DOI: https://doi.org/10.1037/a0035779  

Vogel, E. K., & Machizawa, M. G. (2004). Neural activity predicts individual differences in visual working memory capacity. Nature , 428, 748–751. DOI: https://doi.org/10.1038/nature02447  

Vogel, E. K., McCollough, A. W., & Machizawa, M. G. (2005). Neural measures reveal individual differences in controlling access to working memory. Nature , 438(24), 500–503. DOI: https://doi.org/10.1038/nature04171  

Watanabe, K., & Funahashi, S. (2014). Neural mechanisms of dual-task interference and cognitive capacity limitation in the prefrontal cortex. Nature Neuroscience , 17, 601–611. DOI: https://doi.org/10.1038/nn.3667  

Wickens, C. D. (1980). The structure of attentional ressources. In: R. S. Nickerson (Ed.), Attention & Performance , VIII, (pp. 239–257). Hillsdale, N.J.: Erlbaum.  

Wilhelm, O., Hildebrandt, A., & Oberauer, K. (2013). What is working memory capacity, and how can we measure it? frontiers in Psychology , 4. DOI: https://doi.org/10.3389/fpsyg.2013.00433  

Williams, M., Pouget, P., Boucher, L., & Woodman, G. F. (2013). Visual-spatial attention aids the maintenance of object representations in visual working memory. Memory & Cognition , 41, 698–715. DOI: https://doi.org/10.3758/s13421-013-0296-7  

Wolff, M. J., Jochim, J., Akyürek, E. G., & Stokes, M. G. (2017). Dynamic hidden states underlying working-memory-guided behavior. Nature Neuroscience , 20, 864–871. DOI: https://doi.org/10.1038/nn.4546  

Woodman, G. F., Carlisle, N. B., & Reinhart, R. M. G. (2013). Where do we store the memory representations that guide attention? Journal of Vision , 13, 1–17. DOI: https://doi.org/10.1167/13.3.1  

Woodman, G. F., & Chun, M. M. (2006). The role of working memory and long-term memory in visual search. Visual Cognition , 14, 808–830. DOI: https://doi.org/10.1080/13506280500197397  

Xu, Y., & Chun, M. M. (2006). Dissociable neural mechanisms supporting visual short-term memory for objects. Nature , 440, 91–94. DOI: https://doi.org/10.1038/nature04262  

Yeung, N., Botvinick, M., & Cohen, J. D. (2004). The neural basis of error detection: Conflict monitoring and the error-related negativity. Psychological Review , 111, 931–959. DOI: https://doi.org/10.1037/0033-295X.111.4.931  

Yu, Q., & Shim, W. M. (2017). Occipital, parietal, and frontal cortices selectively maintain task-relevant features of multi-feature objects in visual working memory. NeuroImage , 157, 97–107. DOI: https://doi.org/10.1016/j.neuroimage.2017.05.055  

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• > Journals
• > The Educational and Developmental Psychologist
• > Volume 30 Issue 2
• > Working Memory: The What, the Why, and the How

Article contents

What is working memory an introduction, why is working memory important, how can we diagnose and support working memory, cognitive tests of working memory, working memory: the what, the why, and the how.

Published online by Cambridge University Press:  13 November 2013

Working memory, our ability to work with information, plays an important role in learning from kindergarten to the college years. In this article, we review the what, the why, and the how of working memory. First, we explore the relationship between working memory, short-term memory, and long-term memory. We also investigate research on the link between whether environmental factors, such as financial background and mother's educational level, affect working memory. In the next section — the why of working memory — we compare the predictive nature of working memory and IQ in learning outcomes. While IQ typically measures the knowledge acquired by the student, working memory measures what they do with that knowledge. Working memory skills are linked to key learning outcomes, including reading and math. In the final section, we present classroom strategies to support working memory. We also review current research on the efficacy of working memory training.

The aim of this review is to introduce a cognitive skill that has been linked with learning — working memory. We discuss the relationship between working memory and other related cognitive skills, such as short-term memory and long-term memory. Next, we introduce research on the role of working memory in learning and compare it with verbal and nonverbal IQ skills. We conclude by providing classroom strategies that educators can adopt to support working memory. We also discuss research on the efficacy of working memory training.

Working memory is our ability to work with information (Alloway, Reference Alloway 2010 ). This higher-level skill is involved in directing attention to a task despite distraction or interference (Cowan, Reference Cowan 2006 ; Engle, Tuholski, Laughlin, & Conway, Reference Engle, Tuholski, Laughlin and Conway 1999 ). Working memory is linked to a range of cognitive activities during the school years, from reasoning tasks to verbal comprehension to mathematical skills (see Cowan & Alloway, Reference Cowan, Alloway, Courage and Cowan 2008 , for a review).

Working memory is distinct from short-term memory, which typically refers to remembering information for a brief period, usually a few seconds (Alloway, Gathercole, & Pickering, Reference Alloway, Gathercole and Pickering 2006 ; see McGrew, Reference McGrew 2009 ). We utilise short-term memory when we remember someone's name, or a phone number, or a title of a book. Typically, this information will be forgotten if it is not rehearsed. Imagine that you are driving to a new school for a meeting. You lose your way and stop at a store to ask for directions. You may repeat the information to yourself over and over again as you walk back to your car so you do not forget. At this point, you are using your short-term memory to remember the directions. Now you get back inside your car and start driving. As you recite the directions to yourself, you look around and match them to the road names. Is this where you make that right turn? Where do you make that second left? Now you are using your working memory as you are applying or using the information that you were given. It is much the same in the classroom. When you give a student a set of instructions, they use their short-term memory to repeat it to themselves. However, by the time they get back to their desk and have to carry out the first task in the set of instructions, chances are if they have poor working memory, they will have forgotten what to do. The process of repeating the information and then carrying out the individual steps relies on working memory.

Working memory is also distinct from long-term memory, though there is close relationship between them, much like a two-way street. Long-term memory refers to memories from our childhood, but it also refers to the knowledge that we have accumulated over the years, such as facts about a country, mathematical knowledge, and grammar rules. One goal of working memory is to transfer new information to our long-term memory. For example, if we are planning a trip to a country that we have not visited before, we use our working memory to retain and transfer the knowledge we learn about that country to our long-term memory. In turn, we can draw on long-term memory to form associations between a familiar place and the new country we are about to visit.

Working Memory and the Environment

Given that working memory is linked to long-term knowledge stores, one issue is the extent to which working memory is influenced by environmental factors that contribute to knowledge acquisition. Of particular interest is the impact of socio-economic status (SES) as this has long been linked with school success, and the income-achievement gap is evident in kindergarten and accelerates over time (Kaplan et al., Reference Kaplan, Turrell, Lynch, Everson, Helkela and Salonen 2001 ). However, with respect to working memory, SES does not appear to have a significant impact on performance. For example, children from low-SES areas in South America did not differ significantly from their middle-SES peers in some working memory tests, although their vocabulary scores, reflecting knowledge-based skills, were considerably worse (Engel, Heloisa Dos Santos, & Gathercole, Reference Engel, Heloisa Dos Santos and Gathercole 2008 ). Dutch studies investigating differences between immigrant children that typically reside in low-income areas (indexed by parental educational), and comparatively wealthier native language speakers found that the former group performed at the same level as their native language peers in working memory tests when tested in their own language (Messer, Leseman, Mayo, & Boom, Reference Messer, Leseman, Mayo and Boom 2010 ; also Leseman, Scheele, Mayo, & Messer, Reference Leseman, Scheele, Mayo and Messer 2007 ). Typically, there are multiple indices of SES, such as family income and parental education, though studies of only parental education have reported that SES is not linked to working memory performance (Alloway, Gathercole, Willis, & Adams, Reference Alloway, Gathercole, Willis and Adams 2004 ; Messer et al., Reference Messer, Leseman, Mayo and Boom 2010 ).

However, some researchers have reported a differentiation of working memory performance as a function of SES levels (Noble, McCandliss, & Farah, Reference Noble, McCandliss and Farah 2007 ; Noble, Norman, & Farah, Reference Noble, Norman and Farah 2005 ). There are two explanations for this disparity in the impact of SES on working memory. The first explanation is sample age, as the chronic stress hypothesis suggests that the prolonged exposure to poverty can result in chronic stress, which in turn leads to reductions in working memory performance (Evans & Schamberg, Reference Evans and Schamberg 2009 ). According to this hypothesis, SES would have a greater impact on older children compared to younger ones. This pattern appears to hold true as studies with older samples (10–13 years) found differences in spatial memory (keeping a location in mind) and the n -back task (Farah et al., Reference Farah, Shera, Savage, Betancourt, Giannetta, Brodsky, Malmud and Hurt 2006 ). Evans and Schamberg ( Reference Evans and Schamberg 2009 ) also reported working memory deficits in their 17-year-olds, as a function of SES. In contrast, studies with young populations reported that working memory was relatively unaffected by SES levels (e.g., Alloway et al., Reference Alloway, Gathercole, Willis and Adams 2004 , with British 4- to 5-year-olds; Engel et al., Reference Engel, Heloisa Dos Santos and Gathercole 2008 , with Brazilian 6- to 7-year-olds; Messer et al., Reference Messer, Leseman, Mayo and Boom 2010 , with Dutch 4-year-olds). One exception is a study with first-graders in New York City (Noble et al., Reference Noble, McCandliss and Farah 2007 ; Noble et al., Reference Noble, Norman and Farah 2005 ), which will be discussed further in the following paragraph.

The tasks used to measure working memory can also affect findings as some studies use tasks that are similar to short-term memory ones (e.g., Noble et al., Reference Noble, McCandliss and Farah 2007 ). Such tasks do not involve any processing of information, which is reflective of working memory, and may rely more on knowledge structures (Alloway et al., Reference Alloway, Gathercole and Pickering 2006 ). As a result, performance may be more sensitive to SES variations, which may account for Noble et al.'s findings. Indeed, Farah et al. ( Reference Farah, Shera, Savage, Betancourt, Giannetta, Brodsky, Malmud and Hurt 2006 ) argued that SES has differential effects on tasks associated with different neurocognitive systems. They reported the greatest effects in the left perisylvian/language system (measured by tests of receptive vocabulary and grammar), but no effect on the parietal lobe/spatial cognition (measured by mental rotation tasks).

Working memory is critical for a variety of activities at school, from complex subjects such as reading comprehension, mental arithmetic, and word problems to simple tasks like copying from the board and navigating around school. It is also important from kindergarten (Alloway et al., Reference Alloway, Gathercole, Adams, Willis, Eaglen and Lamont 2005 ) to the tertiary level (Alloway & Gregory, Reference Alloway, Doherty-Sneddon and Forbes 2012 ).

Working Memory and Reading

A key foundational skill in reading success is known as phonological awareness , where the child must dissect a word into its parts, such as rhyming words or words with the same initial sounds, and the ability to name pictures rapidly. A 5-year longitudinal study of several hundred children who were tracked from kindergarten through fourth grade confirmed that phonological awareness skills predicted reading proficiency (Wagner et al., Reference Wagner, Torgesen, Rashotte, Hecht, Barker, Burgess, Donahue and Garon 1997 ).

Working memory is also highly predictive of reading success. In typically developing children, scores on working memory tasks predict reading achievement independently of measures of phonological awareness (Swanson & Beebe-Frankenberger, Reference Swanson and Beebe-Frankenberger 2004 ). One explanation for why working memory is so critical for reading is that we use our ‘Post-it Note’ (or working memory capacity) to keep all the relevant speech sounds in mind, match them up with the corresponding letters, and the combine them to read the words. Indeed, children with reading difficulties have been found to have a limited capacity for processing and storing information (De Jong, Reference De Jong 1998 ), and often show significant and marked decrements on working memory tasks (Siegel & Ryan, Reference Siegel and Ryan 1989 ).

Working Memory and Mathematics

Working memory is also linked to math outcomes: low working memory scores are closely related to poor performance on arithmetic word problems (Swanson & Sachse-Lee, Reference Swanson and Sachse-Lee 2001 ; also Alloway & Passolunghi, Reference Alloway and Passolunghi 2011 ) and poor computational skills (Bull & Scerif, Reference Bull and Scerif 2000 ; Geary, Hoard, & Hamson, Reference Geary, Hoard and Hamson 1999 ).

Although there is also a close relationship between mathematical skills and working memory, this is mediated by the age of the child, as well as the task. Verbal working memory plays a strong role in math skills in 7-year-olds (Bull & Scerif, Reference Bull and Scerif 2001 ) and is also a reliable indicator of mathematical difficulties in the first year of formal schooling (Gersten, Jordan, & Flojo, Reference Gersten, Jordan and Flojo 2005 ). However, once children reach adolescence, working memory is no longer significantly linked to mathematical skills (Reuhkala, Reference Reuhkala 2001 ). One explanation for this change is that verbal working memory plays a crucial role for basic arithmetic (both to learn arithmetic facts and to retain relevant data such as carried digits) but that as children get older other factors, such as number knowledge and strategies, play a greater role (Thevenot & Oakhill, Reference Thevenot and Oakhill 2005 ). Low working memory scores are related to poor computational skills (Bull & Scerif, Reference Bull and Scerif 2001 ; Geary, Hoard, & Hamson, Reference Geary, Hoard and Hamson 1999 ) and poor performance on arithmetic word problems (Swanson & Sachse-Lee, Reference Swanson and Sachse-Lee 2001 ).

Visuo-spatial memory is also closely linked with mathematical skills. It has been suggested that visuo-spatial memory functions as a mental blackboard, supporting number representation, such as place value and alignment in columns, in counting and arithmetic (D'Amico & Guarnera, Reference D'Amico and Guarnera 2005 ; Geary, Reference Geary 1990 ; McLean & Hitch, Reference McLean and Hitch 1999 ). Specific associations have been found between visuo-spatial memory and encoding in problems presented visually (Logie et al., Reference Logie, Gilhooly and Wynn 1994 ), and in multi-digit operations (Heathcote, Reference Heathcote 1994 ). Visuo-spatial memory skills also uniquely predict performance in nonverbal problems, such as sums presented with blocks, in pre-school children (Rasmussen & Bisanz, Reference Rasmussen and Bisanz 2005 ).

Working Memory Versus IQ Footnote 1

Many studies have demonstrated that both IQ and working memory are related to learning. In the research lab, we investigated which is more important. This issue is important so that educators can target and support the cognitive skills that underpin success in learning. In order to investigate how well IQ and working memory would predict reading, writing, and math skills, a group of 5-year-olds ( n = 194) was tested as they started kindergarten and tracked over a 6-year period. The findings at the first time point (age 5) indicated that working memory was a significant predictor of reading, writing and math. Children with high working memory did well in reading, writing, and math; while those with low working memory struggled in these tasks (Alloway et al., Reference Alloway, Gathercole, Adams, Willis, Eaglen and Lamont 2005 ).

The children were tested again when they were 11 years old in order to explore the best predictors of learning outcomes over time — working memory or Verbal IQ/Performance IQ (VIQ/PIQ). They were also tested on standardised tests of language and math. The results indicated that a student's working memory ability at 5 years of age was a significant predictor of language and math scores 6 years later (Alloway & Alloway, Reference Alloway, Gathercole and Elliott 2010 ). This finding is important as it indicates that while IQ is still viewed as a benchmark of success, other skills, such as working memory, may provide more useful information on a student's potential to learn.

Numerous studies have demonstrated that working memory is a distinct skill from IQ (Cain, Oakhill, & Bryant, Reference Cain, Oakhill and Bryant 2004 ; Siegel, Reference Siegel 1988 ), and uniquely predicts learning outcomes. For example, working memory skills predict a child's performance in language and math, even after a child's IQ scores have been statistically accounted (Gathercole, Alloway, Willis & Adams, Reference Gathercole, Alloway, Willis and Adams 2006 ; Nation, Adams, Bowyer-Crane, & Snowling, Reference Nation, Adams, Bowyer-Crane and Snowling 1999 ; Stothard & Hulme, Reference Stothard and Hulme 1992 ; for a review see Swanson & Saez, Reference Swanson, Saez, Swanson, Graham and Harris 2003 ). The importance of working memory in learning is not just limited to children. This same pattern is evidenced at the university level as well: working memory is a better predictor of grades than entrance exams like SAT scores (Engle et al., Reference Engle, Tuholski, Laughlin and Conway 1999 ).

Why is working memory a better predictor of learning than IQ? Working memory tests measure something different from IQ tests: working memory is an indicator of our potential to learn. A common working memory test is to remember a sequence of numbers in the reverse order that it was presented to you. If students struggle in this test, it is not because they do not know how to count, or understand number magnitude. It doesn't even matter whether they can recognise the numbers. If they struggle in this working memory test, it is often because their ‘Post-it Note’ (or working memory capacity) isn't big enough to remember three or four numbers. Working memory is an accurate predictor of learning from kindergarten to college because it measures students’ ability to learn, rather than what they have learned.

In contrast, other measurements like school tests and IQ tests measure knowledge that they have already learned. If students do well on one of these tests, it is because they know the information they are tested on. Likewise, many aspects of IQ tests also measure the knowledge that we have built up. A commonly used measure of IQ is a vocabulary test. If students know the definition of a word like ‘bicycle’ or ‘police’, then they will likely get a high IQ score. However, if they do not know the definitions of these words or perhaps do not articulate them well, this will be reflected in a low IQ score. In this way, IQ tests are very different from working memory tests because they measure how much students know and well they can articulate this knowledge.

One research project involved two different schools: one was in an urban, developed area, while the other was in an underprivileged neighborhood (Alloway, Alloway, & Wootan, Reference Alloway, Alloway and Wootan 2013 ). As part of the project, students’ IQ was tested using a vocabulary test. One of the vocabulary words — police — drew very different responses. Students from the urban school provided definitions relating to safety or uniforms, which corresponded to the examples in the manual. However, those from the underprivileged neighborhood responded with statements such as ‘I don't like police’ or ‘They are bad because they took my dad away’. Although both responses were drawn directly from the children's experiences, only one type of answer matched the IQ manual's definitions. This example illustrates how performance on IQ tests is strongly driven by a child's background and experiences.

Testing Working Memory

How can you detect working memory problems in a student? If we fall and break a leg, a cast is clearly visible. Yet working memory problems are often hidden from family, friends, and even teachers. In interviews with classroom teachers, we found that working memory failure in a student is often overlooked (Alloway, Doherty-Sneddon, & Forbes, Reference Alloway, Doherty-Sneddon and Forbes 2012 ). Instead, the student is often thought of as lazy or unmotivated. Comments such as ‘You are not trying hard enough’ or ‘Stop playing around and just focus’ are often directed towards the student with working memory problems.

Jenny, 14 years, had difficulty staying on task and always seemed to be two steps behind in her class assignments. In Science class, she had to label and remember the planets in the solar system. The next step was to apply this information to an in-class project. However, when the researcher (EC) walked over to her desk, he noticed that she was still working on the labels — an activity that should have been completed the previous day.

She displayed similar behaviour in her English class. When her teacher asked her to express her thoughts on an essay that was just read to the class, she answered with remarks pertaining to the essay read the day before. When the teacher reprimanded her for not paying attention, she seemed confused and did not understand what she had done. Her mind always seemed to be on the previous day's work, and her performance in classes suffered as a result. She was in a vicious cycle of being a day behind because she could not maintain focus long enough to complete any assignments. Not only were her grades suffering, but also she was frequently frustrated due to the constant reprimands and poor performance she dealt with on a regular basis.

Jenny is an example of classroom behaviour that is characteristic of a student with working memory difficulties. It is not uncommon for working memory difficulties to be regarded as attention problems. Students can lack direction, appear unmotivated, or simply disinterested in the activity. The Working Memory Rating Scale (WMRS) is a behavioural rating scale developed for educators to help them easily identify students with working memory deficits. It consists of 20 descriptions of behaviours characteristic of children with working memory deficits. Teachers rate how typical each behavior was of a particular child, using a 4-point scale ranging from (0) not typical at all to (1) occasionally to (2) fairly typical to (3) very typical .

A starting point in developing the items in the WMRS was an observational study of students with low working memory but typical scores in IQ tests. Compared with classmates with average working memory, the low memory students frequently forgot instructions, struggled to cope with tasks involving simultaneous processing and storage, and lost track of their place in complex tasks. The most common consequence of these failures was that the students abandoned the activity without completing it.

As the WMRS focuses solely on working memory–related problems in a single scale, it does not require any training in psychometric assessment prior to use. It is valuable not only as a diagnostic screening tool for identifying children at risk of poor working memory, but also in illustrating both the classroom situations in which working memory failures frequently arise, and the profile of difficulties typically faced by students with working memory difficulties. The scores are normed for each age group, which means that they are representative of typical classroom behavior for each age group. One item in the WMRS is ‘needs regular reminders of each step in the written task’. The classroom teacher has to rate how typical this behaviour is of the student and compare their score to the manual. A 5-year-old would need more reminders than a 10-year-old, which is reflected in the scoring of the WMRS. The scoring is color-coded to make it easy to interpret. For example, a score in the Green range indicates that it is unlikely that the student has a working memory impairment. If a student's score falls in the Yellow range, it is possible that they have a working memory impairment and further assessment is recommended. Scores in the red range indicate the presence of a working memory and targeted support is recommended.

The WMRS has been validated against other behaviour rating scales, such as the Conners Teacher Rating Scale and the BRIEF (Alloway, Elliott, & Place, Reference Alloway and Alloway 2010 ). The WMRS measures behaviour that is different from that represented in these other rating scales, and thus reliably identifies students with poor working memory. The WMRS has also been compared to cognitive tests of working memory, IQ, and academic attainment. The majority of students considered by their teachers to have problematic behaviours (i.e., typical of poor working memory) are more likely to have low working memory scores and achieve low grades (Alloway, Gathercole, & Elliott, Reference Alloway, Elliott and Place 2010 ).

It is important to know that students who display poor working memory behavior will not necessarily have low IQ scores. Many of them can have average IQ scores. Yet, it is working memory overload that leads to all the behaviours we have discussed and their loss of focus in the task can appear to be inattentive and distracted to others. The WMRS enables teachers to use their knowledge of the student to produce an indicator of how likely it is that the child has a working memory problem. Thus, it provides a valuable first step in detecting possible working memory failures.

Mary, 14 years, struggled in writing assignments. If a writing assignment extended over several days, she had a difficult time remembering her train of thought from her previous writing session. She needed hands-on support from her teacher and asked questions frequently to guide her activities. When she was asked to read her writing out aloud, her reading was uncertain and sounded similar to reading an unfamiliar text. She would skip lines and mingle sentences together from different parts of the paper. She required extra attention and guidance in order to complete her assignments.

Educators are growing increasingly aware that students like Mary have working memory difficulties that can impact their learning. Test publishers are also recognising the importance of working memory in education. Many standardised IQ test batteries, such as the Wechsler's Intelligence Scale, Stanford-Binet, and Woodcock Johnson, all include working memory tests as part of their assessment. However, these batteries are limited as they do not include visuo-spatial working memory tests and so do not provide a working memory profile of strengths and difficulties that can inform individualised education plans.

In order to address this need, the Alloway Working Memory Assessment (AWMA; Alloway, Reference Alloway 2007 ) Alloway Working Memory Assessment — 2 (Alloway, Reference Alloway 2012B ) were developed. This standardised battery is fully automated and provides assessments of verbal and visuo-spatial working memory, making it convenient for teachers and educational professionals alike, to screen individuals for significant working memory problems. Working memory tasks involve both remembering and processing information, while short-term memory is assessed using tasks that involve only remembering information. The AWMA uses a variety of stimuli. For example, tests of verbal working memory consist of letters and numbers, and the visual-spatial working memory tests include dot locations and three-dimensional arrays of blocks. Multiple methods of assessing the same underlying aspect of working memory allow the test administrator to distinguish whether a student has a working memory deficit or as difficulty in processing a particular type of stimulus.

The AWMA uses a span procedure, which makes it particularly suitable when testing both children and adults. The number of items to be remembered is increased over successive trials until the individual begins to struggle. Memory span (capacity) is the maximum amount of information that an individual can remember accurately. For example, in the backward digit recall test (verbal working memory), an individual remembers numbers in backwards order. If they are unable to remember five numbers, then the task ends and the individual's memory capacity is four items.

1. The AWMA is quick: It takes 10 minutes

The Screener version of the AWMA can be used to screen students with suspected working memory difficulties. It consists of the following two tests: Processing Letter Recall and Mr X. For a more detailed memory profile of the student, the AWMA also includes a Short Form, which includes tests of verbal short-term memory and visual-spatial short-term memory in addition to the working memory ones in the Screener. The administration time is approximately 20 minutes for all individuals. A Long Form with multiple assessments of each memory component is also included in the AWMA. It takes approximately 30 minutes to administer. One reason that the AWMA is easy for teachers to use is that the program automatically generates a report with standard scores and percentiles that is easy to interpret once the test is completed.

2. The AWMA has good reliability

Test reliability refers to the consistency with which a test can accurately measure what it aims to do. If an individual's performance remains consistent over repeated trials, it is considered to be reliable. Thousands of students have been tested and then re-tested on the AWMA 6 weeks and 1 year apart. The test–retest scores for the AWMA are high, indicating that the AWMA captures the stability of working memory over time (Alloway, Gathercole, Kirkwood, & Elliott, Reference Alloway, Gathercole, Kirkwood and Elliott 2008 ).

3. The AWMA has good validity

Test validity refers to whether a test accurately measures the skills it is designed to measure. In order to establish test validity, I took a group of students with poor working memory as identified by the AWMA and compared their scores in the Wechsler Working Memory Index (WMI; Alloway, Gathercole, Kirkwood, & Elliott, Reference Alloway, Gathercole, Kirkwood and Elliott 2009 ). The majority of students who achieved low scores on the AWMA also scored poorly on the Wechsler WMI. This pattern establishes that AWMA provides a valid measure of working memory.

The AWMA is effective in identifying students at risk. In a study of 3,000 students, the majority of students with poor working memory also scored poorly on a standardised attainment test and vocabulary (Alloway et al., Reference Alloway, Gathercole, Kirkwood and Elliott 2009 ). Scores on the AWMA can also identify those who need extra support in the classroom, as well as those who take longer to process information and so would benefit from extra time during assessments.

Supporting Working Memory Through Strategies

Classroom teachers can make small tweaks in the daily routine of the student to support their learning.

1. Detect working memory failures

Is the student struggling to keep up with their peers? Are they beginning to disengage from the activity? Are they acting out in frustration? Once you have identified these signs in a student, you can follow the next two recommendations.

2. Break down information

If an activity exceeds the working memory capacity of a student, they will be unable to complete the task.

3. Build long-term knowledge

This process can foster automaticity of knowledge in the student, which can ease the likelihood of working memory overload.

The following case studies illustrate how these steps can be implemented in a classroom setting.

Jimmy, 10 years, had difficulty issues recalling information, as well as completing simple tasks. His writing skills were poor and his reading comprehension was also much lower than the rest of his classmates.

1. Detecting working memory failures . When the researcher (EC) reviewed his lesson plans, he realised that the time that the class worked on certain assignments changed every day. Jimmy found it difficult to work within this varying schedule and was frequently frustrated. In order to support his learning and keep him apace with his classmates, the researcher started by developing a structured schedule for him. For example, every day at the same time he would complete writing activities, regardless of what the rest of the class was doing. Now that his day was structured, he knew exactly what to anticipate and was mentally ready to tackle his next assignment. His behaviour improved as a result.

2. Break down information . During writing assignments, the researcher would break down complex sentences and have Jimmy write one sentence at a time. After each paragraph, the researcher would read it aloud to Jimmy and then ask him to read it. Eventually he was able to write multiple sentences at a time without prompting, and read the paragraph aloud to the researcher before it was read to him.

3. Build long-term knowledge . Each day, the researcher would review the multi-syllable words with Jimmy and reinforce meaning. The next day the researcher would ask him what the word meant. The word ‘because’ perplexed him at first. One day, they used it in a sentence and read it together, and the researcher explained the meaning of the word, as well as the proper usage. The next day during Jimmy's writing assignment, he had to use the word in one of his sentences. He was able to use the word effectively and continued to better its usage throughout the week. Developing a schedule, breaking down the writing assignments, and explaining the meaning of words allowed him to catch up with his classmates in writing and reading assignments.

Janine, 11 years, was having difficulty with manipulating three and four digit numbers.

1. Detecting working memory failures . When the researcher (EC) first assessed her ability, he realised she was proficient with two-digit multiplication, but the borrowing system with three digits confused her. The same issue was evident with her long division.

2. Break down information. The researcher began with asking her to add multi-digit numbers together (e.g., 345 + 678). After successfully completing this task, he asked her to multiply the same numbers together, guiding her through each step along the way and talking about each step. They spent a week doing this together. Her homework assignments had to be completed in a quiet room away from televisions and radios. If she had any difficulties with the math problems, she was instructed to make a note next to the problem and move onto the next one (to avoid her adopting incorrect techniques). The following day they would review her notes and the next homework assignment contained problems that mimicked the ones she had difficulties with. Eventually she was able to confidently perform complex multiplication with ease.

The next challenge was long division. The multiplication sessions began by going over simple division (e.g., 16/4) and going through them step by step. Janine began by only dividing numbers that were even, and eventually integrated simple numbers with decimals. After integrating decimals, she was moved on to even-number long division (e.g., 100/5). The researcher went through these step by step with her until she could do them with ease. She began to remember the steps in the process, as well as the rules of long division. Once she demonstrated signs of proficiency in these problems, she moved onto three- and four-digit long division. After she showed progress with those, she was given integrated decimal division. Her homework assignments mirrored what she learned in class, as well as added a few complex problems.

3. Build long-term knowledge. After a month of learning complex multiplication and division, Janine was given an assessment. The assessment required her to write down each step of the process for the multiplication and division problems. She was then given math problems that steadily progressed in difficulty. She improved greatly from the start of our sessions. By starting at the basic level of each process she was able to build the necessary rules to do more complex problems later on. She eventually was teaching herself with ease. By building proper study habits and learning habits she was able to learn and recall information that used to difficult for her.

Recently, there have been several reviews on the efficacy of working memory training (Buschkuehl, Jaeggi, & Jonides, Reference Buschkuehl, Jaeggi and Jonides 2012 ; Jaeggi, Buschkuehl, Jonides, & Shah, Reference Jaeggi, Buschkuehl, Jonides and Shah 2011 ; Morrison & Chein, Reference Morrison and Chein 2011 , for reviews). While there are both positive findings, as well as null results, the key is the working memory program — we cannot apply a blanket statement that all training works (or does not work). Based on research, here are three key things to look for when evaluating the research findings:

1. Control group

A control group offers a comparison to make sure that the training program is not just working because the child is doing something different. Some studies just use a control group who do not do anything. While this is a good start, an ideal control group is a group of people who are doing something different from the training program (such as reading or playing a different computer game). We recently published findings on a working memory training program, Jungle Memory, that included a control group that received additional educational support (Alloway, Reference Alloway 2012a ). The findings indicated that the training group performed significantly better than the control group in standardised tests of IQ and working memory after an 8-week period. Jaeggi et al. ( Reference Jaeggi, Buschkuehl, Jonides and Shah 2011 ) also reported improvements in a non-verbal IQ after a working memory training task ( n -back task) in young adults.

2. Transfer effects

This refers to whether the program improves anything other than getting better at the game itself. Practising one thing will naturally make you better at it. This is known as a practice effect. But can the benefits of a brain training program transfer to real world activities? In other words, can you get better at something other than the training game? In clinical trials, Jungle Memory showed transfer effects — the students showed improvements not just in working memory, but in IQ, and more importantly, in grades as well (Alloway, Reference Alloway 2012a ).

3. Maintenance

How long do the results last? It is important to consider whether the training benefits will last beyond the training period. In a study of almost 100 students, we found that the benefits of Jungle Memory training persisted when students were tested 8 months later (Alloway, Bibile, & Lau, Reference Alloway, Bibile and Lau 2013 ).

In summary, working memory is a foundational skill for learning. It measures our ability to work with information and is linked to learning from kindergarten to the college years. Standardized tests provide accurate and quick ways to assess a student's working memory performance in order to best support their learning. A combination of strategies and training can improve the working memory capacity in our students, thus providing them with an opportunity to reach their potential.

1 Throughout the article the reference to IQ scores is restricted to the Verbal or Performance subscales, rather than the Full Scale IQ score, which does include working memory tests in many of the standardised test batteries (e.g., Wechsler, Woodcock-Johnson, Stanford-Binet).

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• Volume 30, Issue 2
• Tracy Packiam Alloway (a1) and Evan Copello (a1)
• DOI: https://doi.org/10.1017/edp.2013.13

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Annual Review of Psychology

Volume 63, 2012, review article, working memory: theories, models, and controversies.

• Alan Baddeley 1
• View Affiliations Hide Affiliations Affiliations: Department of Psychology, University of York, York YO10 5DD, United Kingdom; email: [email protected]
• Vol. 63:1-29 (Volume publication date January 2012) https://doi.org/10.1146/annurev-psych-120710-100422
• First published as a Review in Advance on September 27, 2011
• © Annual Reviews

I present an account of the origins and development of the multicomponent approach to working memory, making a distinction between the overall theoretical framework, which has remained relatively stable, and the attempts to build more specific models within this framework. I follow this with a brief discussion of alternative models and their relationship to the framework. I conclude with speculations on further developments and a comment on the value of attempting to apply models and theories beyond the laboratory studies on which they are typically based.

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Literature Cited

• Allen R , Baddeley AD , Hitch GJ . 2006 . Is the binding of visual features in working memory resource-demanding?. J. Exp. Psychol.: Gen. 135 : 298– 313 [Google Scholar]
• Alloway TP , Gathercole SE , Pickering SJ . 2006 . Verbal and visuospatial short-term and working memory in children: Are they separable?. Child Dev. 77 : 1698– 716 [Google Scholar]
• Anderson JR , Bothell D , Byrne MD , Douglass S , Lebiere C , Qin Y . 2004 . An integrated theory of the mind. Psychol. Rev. 111 : 1036– 60 [Google Scholar]
• Anderson JR , Reder LM , Lebiere C . 1996 . Working memory: activation limitations on retrieval. Cogn. Psychol. 30 : 221– 56 [Google Scholar]
• Atkinson RC , Shiffrin RM . 1968 . Human memory: a proposed system and its control processes. The Psychology of Learning and Motivation: Advances in Research and Theory KW Spence, JT Spence 89– 195 New York: Academic [Google Scholar]
• Attneave F . 1960 . In defense of homunculi. Sensory Communication W Rosenblith 777– 82 Cambridge, MA: MIT Press [Google Scholar]
• Baars BJ . 1988 . A Cognitive Theory of Consciousness Cambridge, UK: Cambridge Univ. Press [Google Scholar]
• Baddeley AD . 1960 . Enhanced learning of a position habit with secondary reinforcement for the wrong response. Am. J. Psychol. 73 : 454– 57 [Google Scholar]
• Baddeley AD . 1966a . Short-term memory for word sequences as a function of acoustic, semantic and formal similarity. Q. J. Exp. Psychol. 18 : 362– 65 [Google Scholar]
• Baddeley AD . 1966b . The influence of acoustic and semantic similarity on long-term memory for word sequences. Q. J. Exp. Psychol. 18 : 302– 9 [Google Scholar]
• Baddeley AD . 1968 . How does acoustic similarity influence short-term memory?. Q. J. Exp. Psychol. 20 : 249– 64 [Google Scholar]
• Baddeley AD . 1976 . The Psychology of Memory New York: Basic Books [Google Scholar]
• Baddeley AD . 1986 . Working Memory Oxford, UK: Oxford Univ. Press [Google Scholar]
• Baddeley AD . 1996 . Exploring the central executive. Q. J. Exp. Psychol. A 49 : 5– 28 [Google Scholar]
• Baddeley AD . 2000 . The episodic buffer: a new component of working memory?. Trends. Cogn. Sci. 4 : 417– 23 [Google Scholar]
• Baddeley AD . 2007 . Working Memory, Thought and Action Oxford, UK: Oxford Univ. Press [Google Scholar]
• Baddeley AD , Allen RJ , Hitch GJ . 2011 . Binding in visual working memory: the role of the episodic buffer. Neuropsychologia 49 : 1393– 400 [Google Scholar]
• Baddeley AD , Allen RJ , Vargha-Khadem F . 2010 . Is the hippocampus necessary for visual and verbal binding in working memory?. Neuropsychologia 48 : 1089– 95 [Google Scholar]
• Baddeley AD , Andrade J . 2000 . Working memory and the vividness of imagery. J. Exp. Psychol.: Gen. 129 : 126– 45 [Google Scholar]
• Baddeley AD , Ecob JR . 1970 . Simultaneous acoustic and semantic coding in short-term memory. Nature 277 : 288– 89 [Google Scholar]
• Baddeley AD , Gathercole SE , Papagno C . 1998 . The phonological loop as a language learning device. Psychol. Rev. 105 : 158– 73 [Google Scholar]
• Baddeley AD , Grant W , Wight E , Thomson N . 1975a . Imagery and visual working memory. Attention and Performance V PMA Rabbitt, S Dornic 205– 17 London: Academic [Google Scholar]
• Baddeley AD , Hitch GJ . 1974 . Working memory. The Psychology of Learning and Motivation: Advances in Research and Theory GA Bower 47– 89 New York: Academic [Google Scholar]
• Baddeley AD , Hitch GJ . 1977 . Recency re-examined. Attention and Performance VI S Dornic 647– 67 Hillsdale, NJ: Erlbaum [Google Scholar]
• Baddeley AD , Hitch GJ , Allen RJ . 2009 . Working memory and binding in sentence recall. J. Mem. Lang. 61 : 438– 56 [Google Scholar]
• Baddeley AD , Larsen JD . 2007 . The phonological loop unmasked? A comment on the evidence for a “perceptual-gestural” alternative. Q. J. Exp. Psychol. 60 : 497– 504 [Google Scholar]
• Baddeley AD , Levy BA . 1971 . Semantic coding and short-term memory. J. Exp. Psychol. 89 : 132– 36 [Google Scholar]
• Baddeley AD , Lewis V , Vallar G . 1984 . Exploring the articulatory loop. Q. J. Exp. Psychol. A 36 : 233– 52 [Google Scholar]
• Baddeley AD , Lieberman K . 1980 . Spatial working memory. Attention and Performance VIII R Nickerson 521– 39 Hillsdale, NJ: Erlbaum [Google Scholar]
• Baddeley AD , Logie RH . 1999 . Working memory: the multiple component model. Models of Working Memory: Mechanisms of Active Maintenance and Executive Control A Miyake, P Shah 28– 61 Cambridge, UK: Cambridge Univ. Press [Google Scholar]
• Baddeley AD , Papagno C , Vallar G . 1988 . When long-term learning depends on short-term storage. J. Mem. Lang. 27 : 586– 95 [Google Scholar]
• Baddeley AD , Thomson N , Buchanan M . 1975b . Word length and the structure of short-term memory. J. Verbal Learn. Verbal Behav. 14 : 575– 89 [Google Scholar]
• Baddeley AD , Vallar G , Wilson BA . 1987 . Sentence comprehension and phonological memory: some neuropsychological evidence. Attention and Performance XII: The Psychology of Reading M Coltheart 509– 29 London: Erlbaum [Google Scholar]
• Baddeley AD , Vargha-Khadem F , Mishkin M . 2001 . Preserved recognition in a case of developmental amnesia: implications for the acquisition of semantic memory. J. Cogn. Neurosci. 13 : 357– 69 [Google Scholar]
• Baddeley AD , Warrington EK . 1970 . Amnesia and the distinction between long- and short-term memory. J. Verbal Learn. Verbal Behav. 9 : 176– 89 [Google Scholar]
• Barnard PJ . 1985 . Interactive cognitive subsystems: a psycholinguistic approach to short-term memory. Progress in the Psychology of Language A Ellis 197– 258 London: Erlbaum [Google Scholar]
• Barnard PJ . 1987 . Cognitive resources and the learning of human-computer dialogs. Interfacing Thought: Cognitive Aspects of Human-Computer Interaction JM Carroll 112– 58 Cambridge, MA: MIT Press [Google Scholar]
• Barrouillet P , Bernardin S , Camos V . 2004 . Time constraints and resource sharing in adults' working memory spans. J. Exp. Psychol.: Gen. 133 : 83– 100 [Google Scholar]
• Bowman H . 2011 . Is there a future in architectural modeling of mind? Paper presented at Philip Barnard retirement symp., MRC Appl. Psychol. Unit, Cambridge, UK [Google Scholar]
• Braithwaite RB . 1953 . Scientific Explanation Cambridge, UK: Cambridge Univ. Press [Google Scholar]
• Brooks LR . 1968 . Spatial and verbal components in the act of recall. Can. J. Psychol. 22 : 349– 68 [Google Scholar]
• Brown GDA , Preece T , Hulme C . 2000 . Oscillator-based memory for serial order. Psychol. Rev. 107 : 127– 81 [Google Scholar]
• Burgess N , Hitch GJ . 1999 . Memory for serial order: a network model of the phonological loop and its timing. Psychol. Rev. 106 : 551– 81 [Google Scholar]
• Burgess N , Hitch GJ . 2006 . A revised model of short-term memory and long-term learning of verbal sequences. J. Mem. Lang. 55 : 627– 52 [Google Scholar]
• Cermak LS , Butters N , Moreines J . 1974 . Some analyses of the verbal encoding deficit of alcoholic Korsakoff patients. Brain Lang. 1 : 141– 50 [Google Scholar]
• Cermak LS , Reale L . 1978 . Depth of processing and retention of words by alcoholic Korsakoff patients. J. Exp. Psychol. Hum. Learn. 4 : 165– 74 [Google Scholar]
• Colle HA , Welsh A . 1976 . Acoustic masking in primary memory. J. Verbal Learn. Verbal Behav. 15 : 17– 32 [Google Scholar]
• Conrad R , Hull AJ . 1964 . Information, acoustic confusion and memory span. Br. J. Psychol. 55 : 429– 32 [Google Scholar]
• Cowan N . 1999 . An embedded-processes model of working memory. Models of Working Memory A Miyake, P Shah 62– 101 Cambridge, UK: Cambridge Univ. Press [Google Scholar]
• Cowan N . 2005 . Working Memory Capacity. Hove, UK: Psychol. Press [Google Scholar]
• Cowan N , Day L , Saults JS , Keller TA , Johnson T , Flores L . 1992 . The role of verbal output time and the effects of word-length on immediate memory. J. Mem. Lang. 31 : 1– 17 [Google Scholar]
• Craik FIM , Lockhart RS . 1972 . Levels of processing: a framework for memory research. J. Verbal Learn. Verbal Behav. 11 : 671– 84 [Google Scholar]
• Dale HCA . 1973 . Short-term memory for visual information. Br. J. Psychol. 64 : 1– 8 [Google Scholar]
• Daneman M , Carpenter PA . 1980 . Individual differences in working memory and reading. J. Verbal Learn. Verbal Behav. 19 : 450– 66 [Google Scholar]
• Daneman M , Merikle PM . 1996 . Working memory and language comprehension: a meta-analysis. Psychonom. Bull. Rev. 3 : 422– 33 [Google Scholar]
• Della Sala S , Gray C , Baddeley A , Allamano N , Wilson L . 1999 . Pattern span: a tool for unwelding visuo-spatial memory. Neuropsychologia 37 : 1189– 99 [Google Scholar]
• Düzel E , Vargha-Khadem F , Heinze HJ , Mishkin M . 2001 . Brain activity evidence for recognition without recollection after early hippocampal damage. Proc. Natl. Acad. Sci. USA 98 : 8101– 6 [Google Scholar]
• Emerson MJ , Miyake A . 2003 . The role of inner speech in task switching: a dual-task investigation. J. Mem. Lang. 48 : 148– 68 [Google Scholar]
• Engle RW , Kane MJ . 2004 . Executive attention, working memory capacity and two-factor theory of cognitive control. The Psychology of Learning and Motivation B Ross 145– 99 New York: Elsevier [Google Scholar]
• Engle RW , Kane MJ , Tuholski SW . 1999 . Individual differences in working memory capacity and what they tell us about controlled attention, general fluid intelligence, and functions of the prefrontal cortex. Models of Working Memory: Mechanisms of Active Maintenance and Executive Control A Miyake, P Shah 102– 34 New York: Cambridge Univ. Press [Google Scholar]
• Ericsson KA , Kintsch W . 1995 . Long-term working memory. Psychol. Rev. 102 : 211– 45 [Google Scholar]
• Fodor JA . 1983 . The Modularity of Mind Cambridge, MA: MIT Press [Google Scholar]
• Gathercole SE . 1995 . Is nonword repetition a test of phonological memory or long-term knowledge? It all depends on the nonwords. Mem. Cognit. 23 : 83– 94 [Google Scholar]
• Gathercole SE , Alloway TP . 2008 . Working Memory and Learning: A Practical Guide London: Sage [Google Scholar]
• Gathercole SE , Pickering SJ , Ambridge B , Wearing H . 2004a . The structure of working memory from 4 to 15 years of age. Dev. Psychol. 40 : 177– 90 [Google Scholar]
• Gathercole SE , Pickering SJ , Knight C , Stegmann Z . 2004b . Working memory skills and educational attainment: evidence from National Curriculum assessments at 7 and 14 years of age. Appl. Cogn. Psychol. 40 : 1– 16 [Google Scholar]
• Glanzer M . 1972 . Storage mechanisms in recall. The Psychology of Learning and Motivation: Advances in Research and Theory GH Bower 129– 93 New York: Academic [Google Scholar]
• Goldman-Rakic PW . 1988 . Topography of cognition: parallel distributed networks in primate association cortex. Annu. Rev. Neurosci. 11 : 137– 56 [Google Scholar]
• Hannula DE , Tranel D , Cohen NJ . 2006 . The long and the short of it: relational memory impairments in amnesia, even at short lags. J. Neurosci. 26 : 8352– 59 [Google Scholar]
• Henson R . 2001 . Neural working memory. Working Memory in Perspective J Andrade 151– 74 Hove, UK: Psychol. Press [Google Scholar]
• Hitch GJ , Flude B , Burgess N . 2009 . Slave to the rhythm: experimental tests of a model for verbal short-term memory and long-term sequence learning. J. Mem. Lang. 61 : 97– 111 [Google Scholar]
• Hurlstone MJ . 2010 . The problem of serial order in visuospatial short-term memory Unpubl. PhD dissert., Univ. York, UK [Google Scholar]
• Johnson MK , Reeder JA , Raye CL , Mitchell KJ . 2002 . Second thoughts versus second looks: an age-related deficit in reflectively refreshing just-activated information. Psychol. Sci. 13 : 64– 67 [Google Scholar]
• Jones D , Hughes RW , Macken WJ . 2006 . Perceptual organization masquerading as phonological storage: further support for a perceptual-gestural view of short-term memory. J. Mem. Lang. 54 : 265– 81 [Google Scholar]
• Jones DM . 1993 . Objects, streams and threads of auditory attention. Attention: Selection, Awareness and Control AD Baddeley, L Weiskrantz 87– 104 Oxford, UK: Clarendon [Google Scholar]
• Jones DM , Macken WJ . 1993 . Irrelevant tones produce an irrelevant speech effect: implications for phonological coding in working memory. J. Exp. Psychol.: Learn. Mem. Cogn. 19 : 369– 81 [Google Scholar]
• Jones DM , Macken WJ . 1995 . Phonological similarity in the irrelevant speech effect: within- or between-stream similarity?. J. Exp. Psychol.: Learn. Mem. Cogn. 21 : 103– 15 [Google Scholar]
• Jonides J , Lewis RL , Nee DE , Lustig CA , Berman MG , Moore KS . 2008 . The mind and brain of short-term memory. Annu. Rev. Psychol. 59 : 193– 224 [Google Scholar]
• Kaschel R , Logie RH , Kazén M , Della Sala S . 2009 . Alzheimer's disease, but not ageing or depression, affects dual-tasking. J. Neurol. 256 : 1860– 68 [Google Scholar]
• Klauer KC , Zhao Z . 2004 . Double dissociations in visual and spatial short-term memory. J. Exp. Psychol.: Gen. 133 : 355– 81 [Google Scholar]
• Kroll NE , Parks T , Parkinson SR , Bieber SL , Johnson AL . 1970 . Short-term memory while shadowing: recall of visually and aurally presented letters. J. Exp. Psychol. 85 : 220– 24 [Google Scholar]
• Lakatos I . 1976 . Proofs and Reputations Cambridge, UK: Cambridge Univ. Press [Google Scholar]
• Larsen J , Baddeley AD . 2003 . Disruption of verbal STM by irrelevant speech, articulatory suppression and manual tapping: Do they have a common source?. Q. J. Exp. Psychol. A 56 : 1249– 68 [Google Scholar]
• Larsen JD , Baddeley AD , Andrade J . 2000 . Phonological similarity and the irrelevant speech effect: implications for models of short-term verbal memory. Memory 8 : 145– 57 [Google Scholar]
• Lashley KS . 1951 . The problem of serial order in behavior. Cerebral Mechanisms in Behavior: The Hixon Symposium LA Jeffress 112– 36 New York: John Wiley [Google Scholar]
• Logie RH . 1986 . Visuo-spatial processing in working memory. Q. J. Exp. Psychol. 38A : 229– 47 [Google Scholar]
• Logie RH . 1995 . Visuo-Spatial Working Memory Hove, UK: Erlbaum [Google Scholar]
• Logie RH . 2011 . The functional organisation and the capacity limits of working memory. Curr. Dir. Psychol. Sci. 20 : 240– 45 [Google Scholar]
• Logie RH , Cocchini G , Della Sala S , Baddeley A . 2004 . Is there a specific capacity for dual task co-ordination? Evidence from Alzheimer's disease. Neuropsychology 18 : 504– 13 [Google Scholar]
• Logie RH , Della Sala S , Wynn V , Baddeley AD . 2000 . Visual similarity effects in immediate serial recall. Q. J. Exp. Psychol. 53A : 626– 46 [Google Scholar]
• Luck SJ , Vogel EK . 1997 . The capacity of visual working memory for features and conjunctions. Nature 390 : 279– 81 [Google Scholar]
• Luria AR . 1959 . The directive function of speech in development and dissolution, part I. Word 15 : 341– 52 [Google Scholar]
• Milner B . 1971 . Interhemispheric differences in the localization of psychological processes in man. Br. Med. Bull. 27 : 272– 77 [Google Scholar]
• Miyake A , Friedman NP , Emerson MJ , Witzki AH , Howenter A , Wager TD . 2000 . The unity and diversity of executive functions and their contributions to complex “frontal lobe” tasks: a latent variable analysis. Cogn. Psychol. 41 : 49– 100 [Google Scholar]
• Monsell S . 2005 . The chronometrics of task-set control. Measuring the Mind: Speed, Control, and Age J Duncan, L Phillips, P McLeod 161– 90 Oxford, UK: Oxford Univ. Press [Google Scholar]
• Nairne JS . 1990 . A feature model of immediate memory. Mem. Cognit. 18 : 251– 69 [Google Scholar]
• Nairne JS . 2002 . Remembering over the short-term: the case against the standard model. Annu. Rev. Psychol. 53 : 53– 81 [Google Scholar]
• Norman DA , Shallice T . 1986 . Attention to action: willed and automatic control of behaviour. Consciousness and Self-Regulation. Advances in Research and Theory RJ Davidson, GE Schwartz, D Shapiro 1– 18 New York: Plenum [Google Scholar]
• Oberauer K . 2010 . Design for a working memory. Psychol. Learn. Motiv. 51 : 45– 100 [Google Scholar]
• Olson IR , Moore KS , Stark M , Chatterjee A . 2006 . Visual working memory is impaired when the medial temporal lobe is damaged. J. Cogn. Neurosci. 18 : 1087– 97 [Google Scholar]
• Osgood CE . 1949 . The similarity paradox in human learning: a resolution. Psychol. Rev. 56 : 132– 43 [Google Scholar]
• Page MPA , Norris D . 1998 . The primacy model: a new model of immediate serial recall. Psychol. Rev. 105 : 761– 81 [Google Scholar]
• Page MPA , Norris DG . 2003 . The irrelevant sound effect: what needs modeling, and a tentative model. Q. J. Exp. Psychol. 56A : 1289– 300 [Google Scholar]
• Phillips WA . 1974 . On the distinction between sensory storage and short-term visual memory. Percept. Psychophys. 16 : 283– 90 [Google Scholar]
• Phillips WA , Baddeley AD . 1971 . Reaction time and short-term visual memory. Psychon. Sci. 22 : 73– 4 [Google Scholar]
• Popper K . 1959 . The Logic of Scientific Discovery London: Hutchison [Google Scholar]
• Posner MI , Keele SW . 1967 . Decay of visual information from a single letter. Science 158 : 137– 39 [Google Scholar]
• Posner MI , Konick AF . 1966 . Short-term retention of visual and kinesthetic information. Organ. Behav. Hum. Perform. 1 : 71– 86 [Google Scholar]
• Riby LM , Perfect TJ , Stollery B . 2004 . The effects of age and task domain on dual task performance: a meta-analysis. Eur. J. Cogn. Psychol. 16 : 863– 91 [Google Scholar]
• Robbins T , Henderson E , Barker D , Bradley A , Fearneyhough C . et al. 1996 . Working memory in chess. Mem. Cognit. 24 : 83– 93 [Google Scholar]
• Rönnberg J , Rudner M , Ingvar M . 2004 . Neural correlates of working memory for sign language. Cogn. Brain Res. 20 : 165– 82 [Google Scholar]
• Ruchkin DS , Grafman J , Cameron K , Berndt RS . 2003 . Working memory retention systems: a state of activated long-term memory. Behav. Brain Sci. 26 : 709– 77 [Google Scholar]
• Saito S , Miyake A . 2004 . On the nature of forgetting and the processing-storage relationship in reading span performance. J. Mem. Lang. 50 : 425– 43 [Google Scholar]
• Salame P , Baddeley AD . 1986 . Phonological factors in STM: similarity and the unattended speech effect. Bull. Psychonom. Soc. 24 : 263– 65 [Google Scholar]
• Shallice T . 1988 . From Neuropsychology to Mental Structure Cambridge, UK: Cambridge Univ. Press [Google Scholar]
• Shallice T . 2002 . Fractionation of the supervisory system. Principles of Frontal Lobe Function DT Stuss, RT Knight 261– 77 New York: Oxford Univ. Press [Google Scholar]
• Shallice T , Warrington EK . 1970 . Independent functioning of verbal memory stores: a neuropsychological study. Q. J. Exp. Psychol. 22 : 261– 73 [Google Scholar]
• Shrager Y , Levy DA , Hopkins RO , Squire LR . 2008 . Working memory and the organization of brain systems. J. Neurosci. 28 : 4818– 22 [Google Scholar]
• Smith EE , Jonides J . 1997 . Working memory: a view from neuroimaging. Cognit. Psychol. 33 : 5– 42 [Google Scholar]
• Smyth MM , Pendleton LR . 1990 . Space and movement in working memory. Q. J. Exp. Psychol. 42A : 291– 304 [Google Scholar]
• Squire LR . 2004 . Memory systems of the brain: a brief history and current perspective. Neurobiol. Learn. Mem. 82 : 171– 77 [Google Scholar]
• Teasdale JD , Barnard PJ . 1993 . Affect, Cognition and Change: Remodeling Depressive Thought Hove, UK: Erlbaum [Google Scholar]
• Toulmin S . 1953 . The Philosophy of Science London: Hutchison [Google Scholar]
• Towse JN , Hitch GJ , Hutton U . 2000 . On the interpretation of working memory span in adults. Mem. Cognit. 28 : 341– 48 [Google Scholar]
• Unsworth N , Engle RW . 2007 . The nature of individual differences in working memory capacity: active maintenance in primary memory and controlled search from secondary memory. Psychol. Rev. 114 : 104– 32 [Google Scholar]
• Vogel EK , Woodman GF , Luck SJ . 2001 . Storage of features, conjunctions, and objects in visual working memory. J. Exp. Psychol.: Hum. Percept. Perform. 27 : 92– 114 [Google Scholar]
• Waugh NC , Norman DA . 1965 . Primary memory. Psychol. Rev. 72 : 89– 104 [Google Scholar]
• Wheeler ME , Treisman AM . 2002 . Binding in short-term visual memory. J. Exp. Psychol.: Gen. 131 : 48– 64 [Google Scholar]
• Williamson V , Baddeley A , Hitch G . 2010 . Musicians' and nonmusicians' short-term memory for verbal and musical sequences: comparing phonological similarity and pitch proximity. Mem. Cognit. 38 : 163– 75 [Google Scholar]

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Effects and mechanisms of working memory training: a review

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• 1 Department of Psychology, University Research Priority Program "Dynamics of Healthy Aging", University of Zurich, Binzmühlestrasse 14/22, 8050, Zurich, Switzerland, [email protected].
• PMID: 24213250
• DOI: 10.1007/s00426-013-0524-6

Can cognitive abilities such as reasoning be improved through working memory training? This question is still highly controversial, with prior studies providing contradictory findings. The lack of theory-driven, systematic approaches and (occasionally serious) methodological shortcomings complicates this debate even more. This review suggests two general mechanisms mediating transfer effects that are (or are not) observed after working memory training: enhanced working memory capacity, enabling people to hold more items in working memory than before training, or enhanced efficiency using the working memory capacity available (e.g., using chunking strategies to remember more items correctly). We then highlight multiple factors that could influence these mechanisms of transfer and thus the success of training interventions. These factors include (1) the nature of the training regime (i.e., intensity, duration, and adaptivity of the training tasks) and, with it, the magnitude of improvements during training, and (2) individual differences in age, cognitive abilities, biological factors, and motivational and personality factors. Finally, we summarize the findings revealed by existing training studies for each of these factors, and thereby present a roadmap for accumulating further empirical evidence regarding the efficacy of working memory training in a systematic way.

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Effects and mechanisms of working memory training: a review

• Published: 10 November 2013
• Volume 78 , pages 803–820, ( 2014 )

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• Claudia C. von Bastian 1 &
• Klaus Oberauer 1  

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Can cognitive abilities such as reasoning be improved through working memory training? This question is still highly controversial, with prior studies providing contradictory findings. The lack of theory-driven, systematic approaches and (occasionally serious) methodological shortcomings complicates this debate even more. This review suggests two general mechanisms mediating transfer effects that are (or are not) observed after working memory training: enhanced working memory capacity, enabling people to hold more items in working memory than before training, or enhanced efficiency using the working memory capacity available (e.g., using chunking strategies to remember more items correctly). We then highlight multiple factors that could influence these mechanisms of transfer and thus the success of training interventions. These factors include (1) the nature of the training regime (i.e., intensity, duration, and adaptivity of the training tasks) and, with it, the magnitude of improvements during training, and (2) individual differences in age, cognitive abilities, biological factors, and motivational and personality factors. Finally, we summarize the findings revealed by existing training studies for each of these factors, and thereby present a roadmap for accumulating further empirical evidence regarding the efficacy of working memory training in a systematic way.

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• Working Memory

Alloway, T. P. (2009). Working memory, but not IQ, predicts subsequent learning in children with learning difficulties. European Journal of Psychological Assessment, 25 (2), 92–98. doi: 10.1027/1015-5759.25.2.92 .

Google Scholar  

Alloway, T. P., Bibile, V., & Lau, G. (2013). Computerized working memory training: Can it lead to gains in cognitive skills in students? Computers in Human Behavior, 29 , 632–638. doi: 10.1016/j.chb.2012.10.023 .

Ando, J., Ono, Y., & Wright, M. J. (2001). Genetic structure of spatial and verbal working memory. Behavior Genetics, 31 (6), 615–624. doi: 10.1023/A:1013353613591 .

PubMed   Google Scholar  

Anguera, J. A., Bernard, J. A., Jaeggi, S. M., Buschkuehl, M., Benson, B. L., Jennett, S., et al. (2012). The effects of working memory resource depletion and training on sensorimotor adaptation. Behavioural Brain Research, 228 , 107–115. doi: 10.1016/j.bbr.2011.11.040 .

PubMed   PubMed Central   Google Scholar  

Bäckman, L., & Nyberg, L. (2013). Dopamine and training-related working-memory improvement. Neuroscience and Biobehavioral Reviews . doi: 10.1016/j.neubiorev.2013.01.014 (in press).

Bakermans-Kranenburg, M. J., van IJzendoorn, M. H., & Bradley, R. H. (2005). Those who have receive: The Matthew effect in early childhood intervention in the home environment. Review of Educational Research, 75 (1), 1–26.

Barrouillet, P., Bernardin, S., & Camos, V. (2004). Time constraints and resource sharing in adults working memory. Journal of Experimental Psychology: General, 133 (1), 83–100.

Bellander, M., Brehmer, Y., Westerberg, H., Karlsson, S., Fürth, D., Bergman, O., et al. (2011). Preliminary evidence that allelic variation in the LMX1A gene influences training-related working memory improvement. Neuropsychologia, 49 , 1938–1942. doi: 10.1016/j.neuropsychologia.2011.03.021 .

Bergman, O., Håkansson, A., Westberg, L., Belin, A. C., Sydow, O., Olson, L., Holmberg, B., Fratiglioni, L., Bäckman, L., Eriksson, E., & Nissbrandt, H. (2009). Do polymorphisms in transcription factors LMX1A and LMX1B influence the risk for Parkinson’s disease? Journal of Neural Transmission, 116 (3), 333–338. doi: 10.1007/s00702-009-0187-z .

Blokland, G. A. M., McMahon, K. L., Thompson, P. M., Martin, N. G., de Zubicaray, G. I., & Wright, M. J. (2011). Heritability of working memory brain activation. The Journal of Neuroscience, 31 (30), 10882–10890. doi: 10.1523/JNEUROSCI.5334-10.2011 .

Borella, E., Carretti, B., Riboldi, F., & De Beni, R. (2010). Working memory training in older adults: Evidence of transfer and maintenance effects. Psychology and Aging, 25 (4), 767–778. doi: 10.1037/a0020683 .

Brehmer, Y., Westerberg, H., & Bäckman, L. (2012). Working-memory training in younger and older adults: Training gains, transfer, and maintenance. Frontiers in Human Neuroscience, 6 (63), 1–7. doi: 10.3389/fnhum.2012.00063 .

Brehmer, Y., Westerberg, H., Bellander, M., Fürth, D., Karlsson, S., & Bäckman, L. (2009). Working memory plasticity modulated by dopamine transporter genotype. Neuroscience Letters, 467 , 117–120. doi: 10.1016/j.neulet.2009.10.018 .

Brose, A., Schmiedek, F., Lövden, M., Molenaar, P. C. M., & Lindenberger, U. (2010). Adult age differences in covariation of motivation and working memory performance: Contrasting between-person and within-person findings. Research in Human Development, 7 (1), 61–78.

Bueller, J., Aftab, M., Sen, S., Gomez-Hassan, D., Burmeister, M., & Zubieta, J.-K. (2006). BDNF Val 66 Met allele is associated with reduced hippocampal volume in healthy subjects. Biological Psychiatry, 59 , 812–815. doi: 10.1016/j.biopsych.2005.09.022 .

Bundesen, C. (1990). A theory of visual attention. Psychological Review, 97 (4), 523–547. doi: 10.1037/0033-295X.97.4.523 .

Buschkuehl, M., & Jaeggi, S. M. (2010). Improving intelligence: A literature review. Swiss Medical Weekly, 140 (19–20), 266–272.

Carretti, B., Borella, E., & De Beni, R. (2007). Does strategic memory training improve the working memory performance of younger and older adults? Experimental Psychology, 54 (4), 311–320. doi: 10.1027/1618-3169.54.4.311 .

Case, R., Kurland, M., & Goldberg, J. (1982). Operational efficiency and the growth of short-term memory span. Journal of Experimental Child Psychology, 33 , 386–404. doi: 10.1016/0022-0965(82)90054-6 .

Chein, J. M., & Morrison, A. B. (2010). Expanding the mind’s workspace: Training and transfer effects with a complex working memory span task. Psychonomic Bulletin & Review, 17 (2), 193–199. doi: 10.3758/PBR.17.2.193 .

Chooi, W.-T., & Thompson, L. A. (2012). Working memory training does not improve intelligence in healthy young adults. Intelligence, 40 , 531–542. doi: 10.1016/j.intell.2012.07.004 .

Cogmed. (2013). Commentary: “Is Working Memory Training Effective? A meta-analytic review”. Retrieved 05/10, 2013. http://www.cogmed.com/commentary-working-memory-training-effective-metaanalytic-review .

Colzato, L. S., van Muijden, J., Band, G. P. H., & Hommel, B. (2011). Genetic modulation of training and transfer in older adults: BDNF Val66Met polymorphism is associated with wider useful field of view. Frontiers in Psychology, 2 (199), 1–6. doi: 10.3389/fpsyg.2011.00199 .

Conway, A. R. A., & Getz, S. J. (2010). Cognitive ability: Does working memory training enhance intelligence? Current Biology, 20 (8), R362–R364.

Conway, A. R. A., Kane, M. J., Bunting, M. F., Hambrick, D. Z., Wilhelm, O., & Engle, R. W. (2005). Working memory span tasks: A methodological review and user’s guide. Psychonomic Bulletin & Review, 12 (5), 739–786. doi: 10.3758/BF03196772 .

Conway, A. R. A., Kane, M. J., & Engle, R. W. (2003). Working memory capacity and its relation to general intelligence. Trends in Cognitive Sciences, 7 (12), 547–552. doi: 10.1016/j.tics.2003.10.005 .

Cowan, N. (1995). Attention and memory: An integrated framework . New York: Oxford University Press.

Craik, F. I. M., & Bialystok, E. (2006). Cognition through the lifespan: Mechanisms of change. TRENDS in Cognitive Sciences, 10 (3), 131–138. doi: 10.1016/j.tics.2006.01.007 .

Dahlin, E., Nyberg, L., Bäckman, L., & Stigsdotter Neely, A. (2008a). Plasticity of executive functioning in young and older adults: Immediate training gains, transfer, and long-term maintenance. Psychology and Aging, 23 (4), 720–730. doi: 10.1037/a0014296 .

Dahlin, E., Stigsdotter Neely, A., Larsson, A., Bäckman, L., & Nyberg, L. (2008b). Transfer of learning after updating training mediated by the striatum. Science, 320 , 1510–1512. doi: 10.1126/science.1155466 .

Daneman, M., & Carpenter, P. A. (1980). Individual differences in working memory and reading. Journal of Verbal Learning & Verbal Behavior, 19 , 450–466.

Deci, E. L., & Ryan, R. M. (2013). Intrinsic Motivation Inventory. Retrieved 06/13, 2013. http://selfdeterminationtheory.org/questionnaires/10-questionnaires/50 (n.d.).

Derakshan, N., & Eysenck, M. W. (2009). Anxiety, processing efficiency, and cognitive performance. European Psychologist, 14 (2), 168–176. doi: 10.1027/1016-9040.14.2.168 .

Dorbath, L., Hasselhorn, M., & Titz, C. (2011). Aging and executive functioning: A training study on focus-switching. Frontiers in Psychology, 2 (257), 1–12. doi: 10.3389/fpsyg.2011.00257 .

Duckworth, A. L., Quinn, P. D., Lynam, D. R., Loeber, R., & Stouthamer-Loeber, M. (2011). Role of test motivation in intelligence testing. Proceedings of the National Academy of Sciences of the United States of America, 108 (19), 7716–7720. doi: 10.1073/pnas.1018601108 .

Duncan, J. (2010). The multiple-demand (MD) system of the primate brain: Mental programs for intelligent behaviour. TRENDS in Cognitive Sciences, 14 (4), 172–179. doi: 10.1016/j.tics.2010.01.004 .

Dunlosky, J., & Kane, M. J. (2007). The contributions of strategy use to working memory span: A comparison of strategy assessment methods. The Quarterly Journal of Experimental Psychology, 60 (9), 1227–1245. doi: 10.1080/17470210600926075 .

Egan, M. F., Kojima, M., Callicott, J. H., Goldberg, T. E., Kolachana, B. S., Bertolino, A., et al. (2003). The BDNF val66met polymorphism affects activity-dependent secretion of BDNF and human memory and hippocampal function. Cell, 112 , 257–269. doi: 10.1016/S0092-8674(03)00035-7 .

Engle, R. W., Kane, M. J., & Tuholski, S. W. (1999a). Individual differences in working memory capacity and what they tell us about controlled attention, general fluid intelligence, and functions of the prefrontal cortex. In A. Miyake & P. Shah (Eds.), Models of working memory: Mechanisms of active maintenance and executive control (pp. 102–134). Cambridge: Cambridge University Press.

Engle, R. W., Tuholski, S. W., Laughlin, J. E., & Conway, A. R. A. (1999b). Working memory, short-term memory, and general fluid intelligence: A latent-variable approach. Journal of Experimental Psychology: General, 128 (3), 309–331. doi: 10.1037/0096-3445.128.3.309 .

Ericsson, K. A., & Chase, W. G. (1982). Exceptional memory: Extraordinary feats of memory can be matched or surpassed by people with average memories that have been improved by training. American Scientist, 70 (6), 607–615.

Ericsson, K. A., & Kintsch, W. (1995). Long-term working memory. Psychological Review, 102 (2), 211–245. doi: 10.1037/0033-295X.102.2.211 .

Feldman Barrett, L., Tugade, M. M., & Engle, R. W. (2004). Individual difference in working memory capacity and dual-process theories of the mind. Psychological Bulletin, 130 (4), 553–573. doi: 10.1037/0033-2909.130.4.553 .

Folstein, M. F., Folstein, S. E., & McHugh, P. R. (1975). “Mini-mental state”: A practical method for grading the cognitive state of patients for the clinician. Journal of Psychiatric Research, 12 (3), 189–198. doi: 10.1016/0022-3956(75)90026-6 .

Friedman, N. P., Miyake, A., Young, S. E., DeFries, J. C., Corley, R. P., & Hewitt, J. K. (2008). Individual differences in executive functions are almost entirely genetic in origin. Journal of Experimental Psychology: General, 137 (2), 201–225. doi: 10.1037/0096-3445.137.2.201 .

Friling, S., Andersson, E., Thompson, L. H., Jönsson, M. E., Hebsgaard, J. B., Nanou, E., et al. (2009). Efficient production of mesencephalic dopamine neurons by Lmx1a expression in embryonic stem cells. Proceedings of the National Academy of Sciences of the United States of America, 106 (18), 7613–7618. doi: 10.1073/pnas.0902396106 .

Garavan, H. (1998). Serial attention within working memory. Memory & Cognition, 26 (2), 263–276.

Gibson, B. S., Gondoli, D. M., Kronenberger, W. G., Johnson, A. C., Steeger, C. M., & Morrisey, R. A. (2013). Exploration of an adaptive training regimen that can target the secondary memory component of working memory capacity. Memory & Cognition, 41 (5), 726–737. doi: 10.3758/s13421-013-0295-8 .

Gibson, B. S., Kronenberger, W. G., Gondoli, D. M., Johnson, A. C., Morrisey, R. A., & Steeger, C. M. (2012). Component analysis of simple span vs. complex span adaptive working memory exercises: A randomized, controlled trial. Journal of Applied Research in Memory and Cognition, 1 (3), 179–184. doi: 10.1016/j.jarmac.2012.06.005 .

Glenberg, A. M., & Lehmann, T. S. (1980). Spacing repetitions over 1 week. Memory & Cognition, 8 (6), 528–538. doi: 10.3758/BF03213772 .

Halford, G. S., Baker, R., McCredden, J. E., & Bain, J. D. (2005). How many variables can humans process? Psychological Science, 16 (1), 70–76. doi: 10.1111/j.0956-7976.2005.00782.x

Hariri, A. R., Goldberg, T. E., Mattay, V. S., Kolachana, B. S., Callicott, J. H., Egan, M. F., et al. (2003). Brain-derived neurotrophic factor val 66 met polymorphism affects human memory-related hippocampal activity and predicts memory performance. The Journal of Neuroscience, 23 (17), 6690–6694.

Heinz, A., Goldman, D., Jones, D. W., Palmour, R., Hommer, D., Gorey, J. G., et al. (2000). Genotype influences in vivo dopamine transporter availability in human striatum. Neuropsychopharmacology, 22 (2), 133–139. doi: 10.1016/S0893-133X(99)00099-8 .

Heinzel, S., Schulte, S., Onken, J., Duong, Q.-L., Riemer, T. G., Heinz, A., Rapp, M. A. (2013). Working memory training improvements and gains in non-trained cognitive tasks in young and older adults. Aging, Neuropsychology, and Cognition . doi: 10.1080/13825585.2013.790338 (in press).

Hidi, S. (2006). Interest—A unique motivational variable. Educational Research Review, 1 (2), 69–82. doi: 10.1016/j.edurev.2006.09.001 .

Holmes, J., Gathercole, S. E., & Dunning, D. L. (2009). Adaptive training leads to sustained enhancement of poor working memory in children. Developmental Science, 12 (4), F9–F15. doi: 10.1111/j.1467-7687.2009.00848.x .

Holmes, J., Gathercole, S. E., Place, M., Dunning, D. L., Hilton, K., & Elliott, J. (2010). Working memory deficits can be overcome: Impacts of training and medication on working memory in children with ADHD. Applied Cognitive Psychology, 24 (6), 827–836. doi: 10.1002/acp.1589 .

Houben, K., Wiers, R. W., & Jansen, A. (2011). Getting a grip on drinking behavior: Training working memory to reduce alcohol abuse. Psychological Science, 22 (7), 968–975. doi: 10.1177/0956797611412392 .

Jaeggi, S. M., Buschkuehl, M., Jonides, J., & Perrig, W., J. (2008). Improving fluid intelligence with training on working memory. Proceedings of the National Academy of Sciences of the United States of America, 105 (19), 6829–6833. doi: 10.1073/pnas.0801268105 .

Jaeggi, S. M., Studer-Luethi, B., Buschkuehl, M., Su, Y.-F., Jonides, J., & Perrig, W. J. (2010). The relationship between n-back performance and matrix reasoning—implications for training and transfer. Intelligence, 38 (6), 625–635. doi: 10.1016/j.intell.2010.09.001 .

Jausovec, N., & Jausovec, K. (2012). Working memory training: Improving intelligence—changing brain activity. Brain and Cognition, 79 , 96–106. doi: 10.1016/j.bandc.2012.02.007 .

Karbach, J. (2008). Potential and Limits of Executive Control Training. Age Differences in the Near and Far Transfer of Task - Switching Training. Universität des Saarlandes, Saarland, Germany. Retrieved from http://scidok.sulb.uni-saarland.de/volltexte/2008/1772/ .

Karbach, J., & Kray, J. (2009). How useful is executive control training? Age differences in near and far transfer of task-switching training. Developmental Science, 12 (6), 978–990. doi: 10.1111/j.1467-7687.2009.00846.x .

Karbach, J., Mang, S., & Kray, J. (2010). Transfer of task-switching training in older age: The role of verbal processes. Psychology and Aging, 25 (3), 677–683. doi: 10.1037/a0019845 .

Kliegl, R., Smith, J., & Baltes, P. B. (1990). On the locus and process of magnification of age differences during mnemonic training. Developmental Psychology, 26 , 894–904. doi: 10.1037/0012-1649.26.6.894 .

Klingberg, T. (2010). Training and plasticity of working memory. Trends in Cognitive Sciences, 14 , 317–324. doi: 10.1016/j.tics.2010.05.002 .

Klingberg, T. (2012). Is working memory capacity fixed? Journal of Applied Research in Memory and Cognition, 1 (3), 194–196. doi: 10.1016/j.jarmac.2012.07.004 .

Klingberg, T., Fernell, E., Olesen, P. J., Johnson, M., Gustafsson, P., Dahlström, K., et al. (2005). Computerized training of working memory in children with ADHD—A randomized, controlled trial. Journal of the American Academy of Child and Adolescent Psychiatry, 44 (2), 177–186. doi: 10.1097/00004583-200502000-00010 .

Klingberg, T., Forssberg, H., & Westerberg, H. (2002). Training of working memory in children with ADHD. Journal of Clinical and Experimental Neuropsychology, 24 (6), 781–791. doi: 10.1076/jcen.24.6.781.8395 .

Kramer, A. F., & Willis, S. L. (2002). Enhancing the cognitive vitality of older adults. Current Directions in Psychological Science, 11 (5), 173–177.

Kyllonen, P. C., & Christal, R. E. (1990). Reasoning ability (is little more than) working-memory capacity?! Intelligence, 14 , 389–433. doi: 10.1016/S0160-2896(05)80012-1 .

Langer, N., von Bastian, C. C., Wirz, H., Oberauer, K., & Jäncke, L. (2013). The effects of working memory training on functional brain network efficiency. Cortex, 49 (9), 2424–2438. doi: 10.1016/j.cortex.2013.01.008 .

Li, S.-C., Schmiedek, F., Huxhold, O., Röcke, C., Smith, J., & Lindenberger, U. (2008). Working memory plasticity in old age: Practice gain, transfer, and maintenance. Psychology and Aging, 23 (4), 731–742. doi: 10.1037/a0014343 .

Lilienthal, L., Tamez, E., Shelton, J. T., Myerson, J., & Hale, S. (2013). Dual n-back training increases the capacity of the focus of attention. Psychonomic Bulletin & Review, 20 , 135–141. doi: 10.3758/s13423-012-0335-6 .

Lövden, M., Bäckman, L., Lindenberger, U., Schaefer, S., & Schmiedek, F. (2010). A theoretical framework for the study of adult cognitive plasticity. Psychological Bulletin, 136 (4), 659–676. doi: 10.1037/a0020080 .

Lövden, M., Brehmer, Y., Li, S.-C., & Lindenberger, U. (2012). Training-induced compensation versus magnification of individual differences in memory performance. Frontiers in Human Neuroscience, 6 (141). doi: 10.3389/fnhum.2012.00141 .

Lu, B., & Gottschalk, W. (2000). Modulation of hippocampal synaptic transmission and plasticity by neurotrophins. Progress in Brain Research, 128 , 231–241. doi: 10.1016/S0079-6123(00)28020-5 .

Lundqvist, A., Grundström, K., Samuelsson, K., & Rönnberg, J. (2010). Computerized training of working memory in a group of patients suffering from acquired brain injury. Brain Injury, 24 (10), 1173–1183. doi: 10.3109/02699052.2010.498007 .

Lustig, C., Shah, P., Seidler, R., & Reuter-Lorenz, P. A. (2009). Aging, training, and the brain: A review and future directions. Neuropsychology Review, 19 , 504–522. doi: 10.1007/s11065-009-9119-9 .

Martinussen, R., Hayden, J., Hogg-Johnson, S., & Tannock, R. (2005). A meta-analysis of working memory impairment in children with attention-deficit/hyperactivity disorder. Journal of the American Academy of Child and Adolescent Psychiatry, 44 (4), 377–384. doi: 10.1097/01.chi.0000153228.72591.73 .

McArdle, J. J., & Prindle, J. J. (2008). A latent change score analysis of a randomized clinical trial in reasoning training. Psychology and Aging, 23 (4), 702–719. doi: 10.1037/a0014349 .

McCarney, R., Warner, J., Iliffe, S., van Haselen, R., Griffin, M., & Fisher, P. (2007). The Hawthorne effect: A randomised, controlled trial. BMC Medical Research Methodology, 7 (30), 1–8. doi: 10.1186/1471-2288-7-30 .

McNab, F., Varrone, A., Farde, L., Jucaite, A., Bystritsky, P., Forssberg, H., et al. (2009). Changes in cortical dopamine D1 receptor binding associated with cognitive training. Science, 323 , 800–802. doi: 10.1126/science.1166102 .

McNamara, D. S., & Scott, J. L. (2001). Working memory capacity and strategy use. Memory & Cognition, 29 (1), 10–17.

Melby-Lervåg, M., & Hulme, C. (2013). Is working memory training effective? A meta-analytic review. Developmental Psychology, 49 (2), 270–291. doi: 10.1037/a0028228 .

Morrison, A. B., & Chein, J. M. (2011). Does working memory training work? The promise and challenges of enhancing cognition by training working memory. Psychonomic Bulletin & Review, 18 , 46–60. doi: 10.3758/s13423-010-0034-0 .

Mumford, M. D., Constanza, D. P., Baughman, W. A., Threlfall, K. V., & Fleishman, E. A. (1994). Influence of abilities on performance during practice: Effects of massed and distributed practice. Journal of Educational Psychology, 86 (1), 134–144. doi: 10.1037/0022-0663.86.1.134 .

Nakatani, T., Kumai, M., Mizuhara, E., Minaki, Y., & Ono, Y. (2010). Lmx1a and Lmx1b cooperate with Foxa2 to coordinate the specification of dopaminergic neurons and control of floor plate cell differentiation in the developing mesencephalon. Developmental Biology, 339 (1), 101–113. doi: 10.1016/j.ydbio.2009.12.017 .

Oberauer, K. (2002). Access to information in working memory: Exploring the focus of attention. Journal of Experimental Psychology. Learning, Memory, and Cognition, 28 (3), 411–421. doi: 10.1037//0278-7393.28.3.411 .

Oberauer, K. (2006). Is the focus of attention in working memory expanded through practice? Journal of Experimental Psychology. Learning, Memory, and Cognition, 32 (2), 197–214. doi: 10.1037/0278-7393.32.2.197 .

Oberauer, K. (2009). Design for a working memory. In B. Ross (Ed.), The psychology of learning and motivation: advances in research and theory (Vol. 51). New York: Academic Press.

Oberauer, K., Lewandowsky, S., Farrell, S., Jarrold, C., & Greaves, M. (2012). Modeling working memory: An interference model of complex span. Psychonomic Bulletin & Review, 19 , 779–819. doi: 10.3758/s13423-012-0272-4 .

Oberauer, K., Süß, H.-M., Wilhelm, O., & Sander, N. (2007). Individual differences in working memory capacity and reasoning ability. In A. R. A. Conway, C. Jarrold, J. M. Kane, A. Miyake, & J. N. Towse (Eds.), Variation in working memory (pp. 49–75). New York: Oxford University Press.

Oberauer, K., Süß, H.-M., Wilhelm, O., & Wittmann, W. W. (2003). The multiple faces of working memory: Storage, processing, supervision, and coordination. Intelligence, 31 , 167–193.

Oberauer, K., Süß, H.-M., Wilhelm, O., & Wittmann, W. W. (2008). Which working memory functions predict intelligence? Intelligence, 36 , 641–652. doi: 10.1016/j.intell.2008.01.007 .

Oken, B. S., Flegal, K., Zajdel, D., Kishiyama, S., Haas, M., & Peters, D. (2008). Expectancy effect: Impact of pill administration on cognitive performance in healthy seniors. Journal of Clinical and Experimental Neuropsychology, 30 (1), 7–17. doi: 10.1080/13803390701775428 .

Olesen, P. J., Westerberg, H., & Klingberg, T. (2004). Increased prefrontal and parietal activity after training of working memory. Nature Neuroscience, 7 (1), 75–79. doi: 10.1038/nn1165 .

Owen, A. M., Hampshire, A., Grahn, J. A., Stenton, R., Dajani, S., Burns, A. S., et al. (2010). Putting brain training to the test. Nature, 465 , 775–779. doi: 10.1038/nature09042 .

Park, D. C., Lautenschlager, G., Hedden, T., Davidson, N. S., Smith, A. D., & Smith, P. K. (2002). Models of visuospatial and verbal memory across the adult life span. Psychology and Aging, 17 (2), 299–320. doi: 10.1037//0882-7974.17.2.299 .

Penner, I.-K., Vogt, A., Stöcklin, M., Gschwind, L., Opwis, K., & Calabrese, P. (2012). Computerised working memory training in healthy adults: A comparison of two different training schedules. Neuropsychological Rehabilitation, 22 (5), 716–733. doi: 10.1080/09602011.2012.686883 .

Raven, J. C. (1990). Advanced progressive matrices: sets I, II . Oxford: Oxford Psychologists Press.

Redick, T. S., Shipstead, Z., Harrison, T. L., Hicks, K. L., Fried, D. E., Hambrick, D. Z., et al. (2013). No evidence of intelligence improvement after working memory training: A randomized, placebo-controlled study. Journal of Experimental Psychology: General, 142 (2), 359–379. doi: 10.1037/a0029082 .

Richmond, L. L., Morrison, A. B., Chein, J. M., & Olson, I. R. (2011). Working memory training and transfer in older adults. Psychology and Aging, 26 (4), 813–822. doi: 10.1037/a0023631 .

Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55 (1), 68–78. doi: http://www.ncbi.nlm.nih.gov/pubmed/11392867 .

Salminen, T., Strobach, T., & Schubert, T. (2012). On the impacts of working memory training on executive functioning. Frontiers in Human Neuroscience, 6 (166). doi: 10.3389/fnhum.2012.00166 .

Schmidt, R. A., & Bjork, R. A. (1992). New conceptualizations of practice: Common principles in three paradigms suggest new concepts for training. Psychological Science, 3 (4), 207–217.

Schmiedek, F., Lövden, M., & Lindenberger, U. (2010). Hundred days of cognitive training enhance broad cognitive abilities in adulthood: Findings from the COGITO study. Frontiers in Aging Neuroscience, 2 (27), 1–10. doi: 10.3389/fnagi.2010.00027 .

Schmiedek, F., Oberauer, K., Wilhelm, O., Süß, H.-M., & Wittmann, W. W. (2007). Individual differences in components of reaction time distributions and their relations to working memory and intelligence. Journal of Experimental Psychology: General, 136 (3), 414–429. doi: 10.1037/0096-3445.136.3.414 .

Schweizer, S., Hampshire, A., & Dalgleish, T. (2011). Extending brain-training to the affective domain: Increasing cognitive and affective executive control through emotional working memory training. PLoS ONE, 6 (9), e24372. doi: 10.1371/journal.pone.0024372 .

Shaywitz, B. A., Holford, T. R., Holahan, J. M., Fletcher, J. M., Stuebing, K. K., Francis, D. J., et al. (1995). A Matthew effect for IQ but not for reading: Results from a longitudinal study. Reading Research Quarterly, 30 (4), 894–906.

Shipstead, Z., Hicks, K. L., & Engle, R. W. (2012a). Cogmed working memory training: Does the evidence support the claims? Journal of Applied Research in Memory and Cognition, 1 (3), 185–193. doi: 10.1016/j.jarmac.2012.06.003 .

Shipstead, Z., Redick, T. S., & Engle, R. W. (2010). Does working memory training generalize? Psychologica Belgica, 50 (3–4), 245–276.

Shipstead, Z., Redick, T. S., & Engle, R. W. (2012b). Is working memory training effective? Psychological Bulletin, 138 (4), 628–654. doi: 10.1037/a0027473 .

Shiran, A., & Breznitz, Z. (2011). The effect of cognitive training on recall range and speed of information processing in the working memory of dyslexic and skilled readers. Journal of Neurolinguistics, 24 , 524–537. doi: 10.1016/j.jneuroling.2010.12.001 .

Stanovich, K. E. (1986). Matthew effects in reading: Some consequences of individual differences in the acquisition of literacy. Reading Research Quarterly, 26 , 7–29.

Stephenson, C. L., & Halpern, D. F. (2013). Improved matrix reasoning is limited to training on tasks with a visuospatial component. Intelligence, 41 , 341–357. doi: 10.1016/j.intell.2013.05.006 .

Studer-Luethi, B., Jaeggi, S. M., Buschkuehl, M., & Perrig, W. J. (2012). Influence of neuroticism and conscientiousness on working memory training outcome. Personality and Individual Differences, 53 (1), 44–49. doi: 10.1016/j.paid.2012.02.012 .

Süß, H.-M., Oberauer, K., Wittmann, W. W., Wilhelm, O., & Schulze, R. (2002). Working-memory capacity explains reasoning ability—And a little bit more. Intelligence, 30 , 261–288.

Swanson, J. M., Flodman, P., Kennedy, J., Spence, M. A., Moyzis, R., Schuck, S., et al. (2000). Dopamine genes and ADHD. Neuroscience and Biobehavioral Reviews, 24 (1), 21–25. doi: 10.1016/S0149-7634(99)00062-7 .

Thompson, T. W., Waskom, M. L., Garel, K.-L. A., Cardenas-Iniguez, C., Reynolds, G. O., Winter, R., et al. (2013). Failure of working memory training to enhance cognition or intelligence. PLoS ONE, 8 (5), e63614. doi: 10.1371/journal.pone.0063614 .

Tsubomi, H., Fukuda, K., Watanabe, K., & Vogel, E. K. (2013). Neural limits to representing objects still within view. The Journal of Neuroscience, 33 (19), 8257–8263. doi: 10.1523/JNEUROSCI.5348-12.2013 .

Turley-Ames, K. J., & Whitfield, M. M. (2003). Strategy training and working memory task performance. Journal of Memory and Language, 49 , 446–468.

Unsworth, N., & Engle, R. W. (2007). On the division of short-term and working memory: An examination of simple and complex span and their relation to higher order abilities. Psychological Bulletin, 133 (6), 1038–1066. doi: 10.1037/0033-2909.133.6.1038 .

VanNess, S. H., Owens, M. J., & Kilts, C. D. (2005). The variable number of tandem repeats element in DAT1 regulates in vitro dopamine transporter density. BMC Genetics, 6 , 55. doi: 10.1186/1471-2156-6-55 .

Verhaeghen, P., Cerella, J., & Basak, C. (2004). A working memory workout: How to expand the focus of serial attention from one to four items in 10 hours or less. Journal of Experimental Psychology. Learning, Memory, and Cognition, 30 , 1322–1337. doi: 10.1037/0278-7393.30.6.1322 .

Verhaeghen, P., & Marcoen, A. (1996). On the mechanisms of plasticity in young and older adults after instruction in the method of loci: Evidence for an amplification model. Psychology and Aging, 11 (1), 164–178. doi: 10.1037/0882-7974.11.1.164 .

von Bastian, C. C., & Eschen, A. (2013). Impact of training procedures on working memory training and transfer effects (Manuscript in preparation).

von Bastian, C. C., Langer, N., Jäncke, L., & Oberauer, K. (2013a). Effects of working memory training in young and old adults. Memory & Cognition, 41 (4), 611–624. doi: 10.3758/s13421-012-0280-7 .

von Bastian, C. C., Locher, A., & Ruflin, M. (2013). Tatool: a Java-based open-source programming framework for psychological studies. Behavior Research Methods, 45 (1), 108–115. doi: 10.3758/s13428-012-0224-y .

von Bastian, C. C., & Oberauer, K. (2013). Distinct transfer effects of training different facets of working memory capacity. Journal of Memory and Language, 69 , 36–58. doi: 10.1016/j.jml.2013.02.002 .

Wass, S. V., Scerif, G., & Johnson, M. H. (2012). Training attentional control and working memory—Is younger, better? Developmental Review, 32 , 360–387. doi: 10.1016/j.dr.2012.07.001 .

Westerberg, H., Jacobaeus, H., Hirvikoski, T., Clevberger, P., Östensson, M.-L., Bartfai, A., et al. (2007). Computerized working memory training after stroke—A pilot study. Brain Injury, 21 (1), 21–29. doi: 10.1080/02699050601148726 .

Wilms, I. L., Petersen, A., & Vangkilde, S. (2013). Intensive video gaming improves encoding speed to visual short-term memory in young male adults. Acta Psychologica, 142 , 108–118. doi: 10.1016/j.actpsy.2012.11.003 .

Wright, M. J., De Geus, E., Ando, J., Luciano, M., Posthuma, D., Ono, Y., et al. (2001). Genetics of cognition: Outline of a collaborative twin study. Twin Research, 4 (1), 48–56. doi: 10.1375/1369052012146 .

Yesavage, J. A., & Jacob, R. (1984). Effects of relaxation and mnemonics on memory, attention and anxiety in the elderly. Experimental Aging Research, 10 (4), 211–214. doi: 10.1080/03610738408258467 .

Yesavage, J. A., Sheikh, J. I., Friedman, L., & Tanke, E. (1990). Learning mnemonics: Roles of aging and subtle cognitive impairment. Psychology and Aging, 5 (1), 133–137. doi: 10.1037/0882-7974.5.1.133 .

Zinke, K., Zeintl, M., Rose, N. S., Putzmann, J., Pydde, A., & Kliegel, M. (2013). Working memory training and transfer in older adults: Effects of age, baseline performance, and training gains. Developmental Psychology . doi: 10.1037/a0032982 (in press).

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Acknowledgments

Preparation of this article was supported by a grant from the Suzanne and Hans Biäsch Foundation for Applied Psychology to C. C. von Bastian and a grant from the Swiss National Science Foundation to K. Oberauer.

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von Bastian, C.C., Oberauer, K. Effects and mechanisms of working memory training: a review. Psychological Research 78 , 803–820 (2014). https://doi.org/10.1007/s00426-013-0524-6

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Working Memory Model (Baddeley and Hitch)

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The Working Memory Model, proposed by Baddeley and Hitch in 1974, describes short-term memory as a system with multiple components.

It comprises the central executive, which controls attention and coordinates the phonological loop (handling auditory information), and the visuospatial sketchpad (processing visual and spatial information).

Later, the episodic buffer was added to integrate information across these systems and link to long-term memory. This model suggests that short-term memory is dynamic and multifaceted.

Take-home Messages

• Working memory is a limited capacity store for retaining information for a brief period while performing mental operations on that information.
• Working memory is a multi-component system that includes the central executive, visuospatial sketchpad, phonological loop, and episodic buffer.
• Working memory is important for reasoning, learning, and comprehension.
• Working memory theories assume that complex reasoning and learning tasks require a mental workspace to hold and manipulate information.

Atkinson’s and Shiffrin’s (1968) multi-store model was extremely successful in terms of the amount of research it generated. However, as a result of this research, it became apparent that there were a number of problems with their ideas concerning the characteristics of short-term memory.

Fig 1 . The Working Memory Model (Baddeley and Hitch, 1974)

Baddeley and Hitch (1974) argue that the picture of short-term memory (STM) provided by the Multi-Store Model is far too simple.

According to the Multi-Store Model , STM holds limited amounts of information for short periods of time with relatively little processing.  It is a unitary system. This means it is a single system (or store) without any subsystems. Whereas working memory is a multi-component system (auditory and visual).

Therefore, whereas short-term memory can only hold information, working memory can both retain and process information.

Working memory is short-term memory . However, instead of all information going into one single store, there are different systems for different types of information.

Central Executive

Visuospatial sketchpad (inner eye), phonological loop.

• Phonological Store (inner ear) processes speech perception and stores spoken words we hear for 1-2 seconds.
• Articulatory control process (inner voice) processes speech production, and rehearses and stores verbal information from the phonological store.

Fig 2 . The Working Memory Model Components (Baddeley and Hitch, 1974)

The labels given to the components (see Fig 2) of the working memory reflect their function and the type of information they process and manipulate.

The phonological loop is assumed to be responsible for the manipulation of speech-based information, whereas the visuospatial sketchpad is assumed to be responsible for manipulating visual images.

The model proposes that every component of working memory has a limited capacity, and also that the components are relatively independent of each other.

The Central Executive

The central executive is the most important component of the model, although little is known about how it functions.  It is responsible for monitoring and coordinating the operation of the slave systems (i.e., visuospatial sketchpad and phonological loop) and relates them to long-term  memory (LTM).

The central executive decides which information is attended to and which parts of the working memory to send that information to be dealt with. For example, two activities sometimes come into conflict, such as driving a car and talking.

Rather than hitting a cyclist who is wobbling all over the road, it is preferable to stop talking and concentrate on driving. The central executive directs attention and gives priority to particular activities.

p> The central executive is the most versatile and important component of the working memory system. However, despite its importance in the working-memory model, we know considerably less about this component than the two subsystems it controls.

Baddeley suggests that the central executive acts more like a system which controls attentional processes rather than as a memory store.  This is unlike the phonological loop and the visuospatial sketchpad, which are specialized storage systems. The central executive enables the working memory system to selectively attend to some stimuli and ignore others.

Baddeley (1986) uses the metaphor of a company boss to describe the way in which the central executive operates.  The company boss makes decisions about which issues deserve attention and which should be ignored.

They also select strategies for dealing with problems, but like any person in the company, the boss can only do a limited number of things at the same time. The boss of a company will collect information from a number of different sources.

If we continue applying this metaphor, then we can see the central executive in working memory integrating (i.e., combining) information from two assistants (the phonological loop and the visuospatial sketchpad) and also drawing on information held in a large database (long-term memory).

The Phonological Loop

The phonological loop is the part of working memory that deals with spoken and written material. It consists of two parts (see Figure 3).

The phonological store (linked to speech perception) acts as an inner ear and holds information in a speech-based form (i.e., spoken words) for 1-2 seconds. Spoken words enter the store directly. Written words must first be converted into an articulatory (spoken) code before they can enter the phonological store.

Fig 3 . The phonological loop

The articulatory control process (linked to speech production) acts like an inner voice rehearsing information from the phonological store. It circulates information round and round like a tape loop. This is how we remember a telephone number we have just heard. As long as we keep repeating it, we can retain the information in working memory.

The articulatory control process also converts written material into an articulatory code and transfers it to the phonological store.

The Visuospatial Sketchpad

The visuospatial sketchpad ( inner eye ) deals with visual and spatial information. Visual information refers to what things look like. It is likely that the visuospatial sketchpad plays an important role in helping us keep track of where we are in relation to other objects as we move through our environment (Baddeley, 1997).

As we move around, our position in relation to objects is constantly changing and it is important that we can update this information.  For example, being aware of where we are in relation to desks, chairs and tables when we are walking around a classroom means that we don”t bump into things too often!

The sketchpad also displays and manipulates visual and spatial information held in long-term memory. For example, the spatial layout of your house is held in LTM. Try answering this question: How many windows are there in the front of your house?

You probably find yourself picturing the front of your house and counting the windows. An image has been retrieved from LTM and pictured on the sketchpad.

Evidence suggests that working memory uses two different systems for dealing with visual and verbal information. A visual processing task and a verbal processing task can be performed at the same time.

It is more difficult to perform two visual tasks at the same time because they interfere with each other and performance is reduced. The same applies to performing two verbal tasks at the same time. This supports the view that the phonological loop and the sketchpad are separate systems within working memory.

The Episodic Buffer

The original model was updated by Baddeley (2000) after the model failed to explain the results of various experiments. An additional component was added called the episodic buffer.

The episodic buffer acts as a “backup” store which communicates with both long-term memory and the components of working memory.

Fig 3 . Updated Model to include the Episodic Buffer

Critical Evaluation

Researchers today generally agree that short-term memory is made up of a number of components or subsystems. The working memory model has replaced the idea of a unitary (one part) STM as suggested by the multistore model.

The working memory model explains a lot more than the multistore model. It makes sense of a range of tasks – verbal reasoning, comprehension, reading, problem-solving and visual and spatial processing. The model is supported by considerable experimental evidence.

The working memory applies to real-life tasks:

• reading (phonological loop)
• problem-solving (central executive)
• navigation (visual and spatial processing)

The KF Case Study supports the Working Memory Model. KF suffered brain damage from a motorcycle accident that damaged his short-term memory.

KF’s impairment was mainly for verbal information – his memory for visual information was largely unaffected. This shows that there are separate STM components for visual information (VSS) and verbal information (phonological loop).

The working memory model does not over-emphasize the importance of rehearsal for STM retention, in contrast to the multi-store model.

Empirical Evidence for Working Memory

What evidence is there that working memory exists, that it comprises several parts, that perform different tasks? Working memory is supported by dual-task studies (Baddeley and Hitch, 1976).

The working memory model makes the following two predictions:

1 . If two tasks make use of the same component (of working memory), they cannot be performed successfully together. 2 . If two tasks make use of different components, it should be possible to perform them as well as together as separately.

Key Study: Baddeley and Hitch (1976)

Aim : To investigate if participants can use different parts of working memory at the same time.

Method : Conducted an experiment in which participants were asked to perform two tasks at the same time (dual task technique) – a digit span task which required them to repeat a list of numbers, and a verbal reasoning task which required them to answer true or false to various questions (e.g., B is followed by A?).

Results : As the number of digits increased in the digit span tasks, participants took longer to answer the reasoning questions, but not much longer – only fractions of a second.  And, they didn”t make any more errors in the verbal reasoning tasks as the number of digits increased.

Conclusion : The verbal reasoning task made use of the central executive and the digit span task made use of the phonological loop.

Brain Imaging Studies

Several neuroimaging studies have attempted to identify distinct neural correlates for the phonological loop and visuospatial sketchpad posited by the multi-component model.

For example, some studies have found that tasks tapping phonological storage tend to activate more left-hemisphere perisylvian language areas, whereas visuospatial tasks activate more right posterior regions like the parietal cortex (Smith & Jonides, 1997).

However, the overall pattern of results remains complex and controversial. Meta-analyses often fail to show consistent localization of verbal and visuospatial working memory (Baddeley, 2012).

There is significant overlap in activation, which may reflect binding processes through the episodic buffer, as well as common executive demands.

Differences in paradigms and limitations of neuroimaging methodology further complicate mapping the components of working memory onto distinct brain regions or circuits (Henson, 2001).

While neuroscience offers insight into working memory, Baddeley (2012) argues that clear anatomical localization is unlikely given the distributed and interactive nature of working memory. Specifically, he suggests that each component likely comprises a complex neural circuit rather than a circumscribed brain area.

Additionally, working memory processes are closely interrelated with other systems for attention, perception and long-term memory . Thus, neuroimaging provides clues but has not yet offered definitive evidence to validate the separable storage components posited in the multi-component framework.

Further research using techniques with higher spatial and temporal resolution may help better delineate the neural basis of verbal and visuo-spatial working memory.

Lieberman (1980) criticizes the working memory model as the visuospatial sketchpad (VSS) implies that all spatial information was first visual (they are linked).

However, Lieberman points out that blind people have excellent spatial awareness, although they have never had any visual information. Lieberman argues that the VSS should be separated into two different components: one for visual information and one for spatial.

There is little direct evidence for how the central executive works and what it does. The capacity of the central executive has never been measured.

Working memory only involves STM, so it is not a comprehensive model of memory (as it does not include SM or LTM).

The working memory model does not explain changes in processing ability that occur as the result of practice or time.

State-based models of WM

Early models of working memory proposed specialized storage systems, such as the phonological loop and visuospatial sketchpad, in Baddeley and Hitch’s (1974) influential multi-component model.

However, newer “state-based” models suggest working memory arises from temporarily activating representations that already exist in your brain’s long-term memory or perceptual systems.

For example, you activate your memory of number concepts to remember a phone number. Or, to remember where your keys are, you activate your mental map of the room.

According to state-based models, you hold information in mind by directing your attention to these internal representations. This gives them a temporary “boost” of activity.

More recent state-based models argue against dedicated buffers, and propose that working memory relies on temporarily activating long-term memory representations through attention (Cowan, 1995; Oberauer, 2002) or recruiting perceptual and motor systems (Postle, 2006; D’Esposito, 2007).

Evidence from multivariate pattern analysis (MVPA) of fMRI data largely supports state-based models, rather than dedicated storage buffers.

For example, Lewis-Peacock and Postle (2008) showed MVPA classifiers could decode information being held in working memory from patterns of activity associated with long-term memory for that content.

Other studies have shown stimulus-specific patterns of activity in sensory cortices support the retention of perceptual information (Harrison & Tong, 2009; Serences et al., 2009).

Atkinson, R. C., & Shiffrin, R. M. (1968). Chapter: Human memory: A proposed system and its control processes. In Spence, K. W., & Spence, J. T. The psychology of learning and motivation (Volume 2). New York: Academic Press. pp. 89–195.

Baddeley, A. D. (1986). Working memory . Oxford: Oxford University Press.

Baddeley, A. (1996). Exploring the central executive.  The Quarterly Journal of Experimental Psychology Section A ,  49 (1), 5-28.

Baddeley, A. D. (2000). The episodic buffer: A new component of working memory? Trends in Cognitive Sciences , 4, (11): 417-423.

Baddeley, A. (2010). Working memory.  Current biology ,  20 (4), R136-R140.

Baddeley, A. (2012). Working memory: Theories, models, and controversies.  Annual review of psychology ,  63 , 1-29.

Baddeley, A. D., & Hitch, G. (1974). Working memory. In G.H. Bower (Ed.), The psychology of learning and motivation: Advances in research and theory (Vol. 8, pp. 47–89). New York: Academic Press.

Baddeley, A. D., & Lieberman, K. (1980). Spatial working memory. ln R. Nickerson. Attention and Performance, VIII . Hillsdale, N): Erlbaum.

Borella, E., Carretti, B., Sciore, R., Capotosto, E., Taconnat, L., Cornoldi, C., & De Beni, R. (2017). Training working memory in older adults: Is there an advantage of using strategies?.  Psychology and Aging ,  32 (2), 178.

Chai, W. J., Abd Hamid, A. I., & Abdullah, J. M. (2018). Working memory from the psychological and neurosciences perspectives: a review.  Frontiers in Psychology ,  9 , 401.

Cowan, N. (1995). Attention and memory: An integrated framework . Oxford Psychology Series, No. 26. New York: Oxford University Press.

Cowan, N. (2005). Working memory capacity.  Exp. Psychology,  54, 245–246.

Cowan, N. (2008). What are the differences between long-term, short-term, and working memory?.  Progress in brain research ,  169 , 323-338.

Curtis, C.E., & D’Esposito, M. (2003). Persistent activity in the prefrontal cortex during working memory. Trends in Cognitive Sciences, 7 (9), 415-423.

D’Esposito, M. (2007). From cognitive to neural models of working memory. Philosophical Transactions of the Royal Society B: Biological Sciences, 362 (1481), 761-772.

D’Esposito, M., & Postle, B. R. (2015). The cognitive neuroscience of working memory.  Annual review of psychology ,  66 , 115-142.

Fell, J., & Axmacher, N. (2011). The role of phase synchronization in memory processes. Nature Reviews Neuroscience, 12 (2), 105-118.

Harrison, S. A., & Tong, F. (2009). Decoding reveals the contents of visual working memory in early visual areas. Nature, 458 (7238), 632-635.

Henson, R. (2001). Neural working memory. In J. Andrade (Ed.), Working memory in perspective (pp. 151-174). Psychology Press.

Lewis-Peacock, J. A., & Postle, B. R. (2008). Temporary activation of long-term memory supports working memory. Journal of Neuroscience, 28 (35), 8765-8771.

Lieberman, M. D. (2000). Introversion and working memory: Central executive differences.  Personality and Individual Differences ,  28 (3), 479-486.

Osaka, M., Osaka, N., Kondo, H., Morishita, M., Fukuyama, H., Aso, T., & Shibasaki, H. (2003). The neural basis of individual differences in working memory capacity: an fMRI study.  NeuroImage ,  18 (3), 789-797.

Serences, J.T., Ester, E.F., Vogel, E.K., & Awh, E. (2009). Stimulus-specific delay activity in human primary visual cortex. Psychological Science, 20( 2), 207-214.

Smith, E.E., & Jonides, J. (1997). Working memory: A view from neuroimaging. Cognitive Psychology, 33 (1), 5-42.

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Q&A: Out with the old, in with the new—the science behind memory clutter and aging

by Katherine Gianni, Boston University

Ever feel like those catchy song lyrics or random pieces of trivia won't leave your head, and it's affecting your memory? Boston University associate professor of psychological & brain sciences Dr. Rob Reinhart, along with his postdoctoral associate, Dr. Wen Wen, dive into this issue in their new PLOS Biology study . Together with their research collaborators, they explore how mental clutter—the stuff we can't seem to forget—affects our memory as we get older.

The research highlights how aging brains struggle to clear out outdated or irrelevant information , leading to slower processing and more frequent forgetfulness. The study identifies a specific brain pattern—beta frequency variability—that predicts memory performance in older adults, while younger adults show a stronger link between memory and their ability to hold onto relevant information. These findings not only give us a clearer picture of how memory changes with age but also point to broader insights into cognitive health and mental well-being.

In this Q&A, Dr. Wen breaks down the study's key findings, explaining the significance of beta-band neural oscillations in memory, how aging impacts the brain's ability to let go of irrelevant information, and what this could mean for future interventions to support healthy cognitive aging. Wen received her Bachelor of Science from Beijing Normal University and Doctor of Philosophy from Peking University. She studies neural plasticity and cognitive aging, with a particular focus on age-related working memory decline.

What is 'working memory' and why is it important?

Working memory is the ability to hold and manipulate information over short periods. It's like a mental workspace where we temporarily keep, edit and remove data to achieve our goals. This cognitive ability is crucial because it helps us perform tasks that require reasoning, problem-solving and planning. However, as we age , working memory tends to decline. While some decline is normal, significant deterioration in working memory can be associated with conditions like dementia.

In this research, you talk about two important working memory processes: maintenance and deletion. What are the differences between the two?

While often referred to as a single cognitive function, working memory actually involves multiple processes. Two key processes are maintenance and deletion. Maintenance refers to the process of actively keeping and holding information. It ensures that this information is readily available to guide our decisions and responses. Because working memory has a limited capacity, we should try to maintain relevant information that is essential for decision-making.

Deletion, on the other hand, involves removing outdated or irrelevant information from working memory. This process is vital for keeping our working memory flexible and efficient. By deleting information that is no longer useful, we reduce interference of pertinent information and prevent irrelevant information from cluttering our capacity-limited system. In summary, while maintenance helps us keep relevant information accessible, deletion ensures that irrelevant information is removed to avoid cognitive overload.

The study also mentions, 'beta-band neural oscillations.' What are those, and why are they significant for working memory?

Neurons are the primary units of brain communication. When populations of neurons are excited or inhibited, rhythmic patterns of activity emerge, which we refer to as neural oscillations. Grouping of neural oscillations can be recorded in electroencephalogram (EEG). Different frequency bands of neural oscillations are associated with various cognitive processes. Beta-band neural oscillations occur in the frequency range of 15–25 Hz. These oscillations have been well-studied for their role in sensorimotor control, but they are also emerging as important for regulating working memory.

Specifically, beta-band oscillations are thought to help modulate the status of working memory contents. Although much of the existing research on beta-band oscillations comes from studies in non-human animals, our study extends this knowledge to humans. We investigated how the dynamic changes of beta-band oscillations help to maintain and delete the contents in working memory.

Your research looks at differences between younger and older adults. What did you learn about how age affects working memory?

We found that working memory performance of younger and older adults are determined by different processes. For younger adults, beta-band neural oscillations during the maintenance stage predicted individual memory performance.

In other words, how well they could hold the information affects working memory performance. In contrast, beta-band neural oscillation during the deletion phase determined working memory performance for older adults. This suggests that older adults' ability to delete outdated information predicts their working memory performance. These findings support the inhibition deficit theory of aging, which says that difficulties in deleting unnecessary information contribute to age-related cognitive decline.

Our study indicates that aging affects working memory processes differently, with deletion deficits being particularly impactful and disruptive for older adults.

How do your findings help us understand cognitive decline as we age?

Studying cognitive aging presents unique challenges, especially when it comes to understanding complex processes like working memory, which is crucial for higher-level cognitive functions. Our research delves into this by breaking down working memory into its individual subprocesses and examining how aging affects each component.

We discovered that older adults face significant challenges specifically with the deletion of irrelevant information. This process, often overshadowed by research focusing on maintenance, appears to be a critical factor in cognitive decline.

The inability to efficiently remove outdated or irrelevant information creates a bottleneck in working memory. This bottleneck can impact the ability to maintain and process information effectively in subsequent tasks. In other words, difficulties in the deletion phase can interfere with memory maintenance, highlighting how these subprocesses are inter-dependent.

By focusing on these distinct subprocesses, our findings provide a more nuanced understanding of cognitive decline. They suggest that interventions should not only target maintenance but also address the specific challenges associated with deletion to more effectively support cognitive health in aging.

Were there any findings in this study that surprised you?

Yes, one of the most surprising findings was the dissociation in working memory functions between younger and older adults. We discovered distinctive processes predicting working memory performance between the two age groups. For younger adults, maintenance functions were key predictors of performance, while for older adults, it was the ability to delete irrelevant information that mattered most.

In fact, we did observe maintenance deficits in older adults; these did not have a significant impact on their behavioral performance. This emphasizes the complexity of cognitive aging and suggests that our current methods of intervention may need to be re-evaluated. It calls for a more nuanced approach to understanding and addressing age-related cognitive decline, focusing not just on general cognitive functions but also on specific processes that are differentially affected by aging.

What practical implications could this research have?

Understanding the mechanisms behind age-related working memory deficits opens up several practical avenues, especially for developing non-pharmacological interventions. While much of the current research on working memory training has focused on improving maintenance functions, our findings suggest that targeting the impaired deletion function could lead to more effective improvements in working memory performance for older adults .

Additionally, our research highlights the role of beta activity as a neural signature for the deletion process. This insight could pave the way for new approaches in brain training and rehabilitation. For example, non-invasive neuromodulation techniques, which are being explored in our lab, could potentially be used to enhance or restore the deletion functions in working memory by modulating beta-band neural oscillations.

Who are your key research collaborators?

Reinhart Lab members. For this study, Shrey Grover and Rob Reinhart, in particular.

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Open Access

Peer-reviewed

Research Article

Beta-band neural variability reveals age-related dissociations in human working memory maintenance and deletion

Roles Conceptualization, Data curation, Formal analysis, Software, Visualization, Writing – original draft

Affiliation Department of Psychological and Brain Sciences, Boston University, Boston, Massachusetts, United States of America

Roles Conceptualization, Formal analysis, Writing – review & editing

Roles Data curation, Formal analysis

Affiliation Tufts University, Department of Biology, Medford, Massachusetts, United States of America

Roles Data curation

Roles Formal analysis

Roles Data curation, Software

Roles Conceptualization, Funding acquisition, Supervision, Writing – review & editing

* E-mail: [email protected]

Affiliations Department of Psychological and Brain Sciences, Boston University, Boston, Massachusetts, United States of America, Department of Biomedical Engineering, Boston University, Boston, Massachusetts, United States of America, Center for Systems Neuroscience, Boston University, Boston, Massachusetts, United States of America, Cognitive Neuroimaging Center, Boston University, Boston, Massachusetts, United States of America, Center for Research in Sensory Communication and Emerging Neural Technology, Boston University, Boston, Massachusetts, United States of America

• Wen Wen, 
• Shrey Grover, 
• Douglas Hazel, 
• Peyton Berning, 
• Frederik Baumgardt, 
• Vighnesh Viswanathan, 
• Olivia Tween, 
• Robert M. G. Reinhart

• Published: September 11, 2024
• https://doi.org/10.1371/journal.pbio.3002784
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Maintaining and removing information in mind are 2 fundamental cognitive processes that decline sharply with age. Using a combination of beta-band neural oscillations, which have been implicated in the regulation of working memory contents, and cross-trial neural variability, an undervalued property of brain dynamics theorized to govern adaptive cognitive processes, we demonstrate an age-related dissociation between distinct working memory functions—information maintenance and post-response deletion. Load-dependent decreases in beta variability during maintenance predicted memory performance of younger, but not older adults. Surprisingly, the post-response phase emerged as the predictive locus of working memory performance for older adults, with post-response beta variability correlated with memory performance of older, but not younger adults. Single-trial analysis identified post-response beta power elevation as a frequency-specific signature indexing memory deletion. Our findings demonstrate the nuanced interplay between age, beta dynamics, and working memory, offering valuable insights into the neural mechanisms of cognitive decline in agreement with the inhibition deficit theory of aging.

Citation: Wen W, Grover S, Hazel D, Berning P, Baumgardt F, Viswanathan V, et al. (2024) Beta-band neural variability reveals age-related dissociations in human working memory maintenance and deletion. PLoS Biol 22(9): e3002784. https://doi.org/10.1371/journal.pbio.3002784

Academic Editor: Frank Tong, Vanderbilt University, UNITED STATES OF AMERICA

Received: December 18, 2023; Accepted: August 2, 2024; Published: September 11, 2024

Copyright: © 2024 Wen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: Object stimuli are available at https://bradylab.ucsd.edu/stimuli.html . Scripts and source data are available at Zenodo, DOI 10.5281/zenodo.12735828 , at https://doi.org/10.5281/zenodo.12735828 .

Funding: RMGR is supported by grants from the National Institutes of Health (R01-MH114877; R01-AG063775; R01-AG082645), the International Obsessive-Compulsive Disorder Foundation (IOCDF), the AE Research Foundation, and philanthropy. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Abbreviations: BEM, boundary element method; CRUNCH, Compensation-Related Utilization of Neural Circuits Hypothesis; EEG, electroencephalography; GLMM, generalized linear mixed model; ICA, independent component analysis; ISI, interstimulus interval; LCMV, linearly constrained minimum variance; RT, reaction time; SNR, signal-to-noise ratio

Introduction

Working memory is a basic cognitive function markedly affected by aging [ 1 , 2 ]. Efficient working memory function is facilitated by multiple processes. On the one hand, processes that promote maintenance of information are important [ 3 ]. Emerging research has identified the neural mechanisms contributing to maintenance deficits with age [ 4 ]. On the other hand, processes that remove information when it loses its relevance are equally important [ 5 ]. Failure to remove irrelevant thoughts from mind can obstruct our capacity-limited systems and interfere with the maintenance of relevant information [ 6 , 7 ]. In fact, a leading theory of neurocognitive aging—the inhibition deficit theory—suggests that impairments in the ability to delete information from working memory are what primarily contribute to age-related decline [ 8 ]. Despite the considerable body of work on age-related deletion deficits in distractor inhibition [ 9 ], only limited attention has been given to the deletion of targets after responses, with no reference to the underlying neural mechanisms.

Beyond its well-studied role in sensorimotor control, rhythmic neural activity in the beta band (15 to 25 Hz) has been suggested to regulate the status of working memory contents [ 10 – 12 ]. Dynamics in beta-band activity reflect working memory processing. There is a decrease in beta activity when information needs to be maintained and an increase when information needs to be deleted [ 13 ]. Maintenance-related beta decrease is primarily observed in the prefrontal cortex [ 13 , 14 ]. By contrast, post-response beta increase is observed among task-related networks involving frontal and centroparietal regions [ 10 ], facilitating removal of both memory contents and associated representations such as motor plans after responses. Specifically, neurophysiological evidence from nonhuman primates demonstrated localized post-response beta increase at sites containing memory information during the time course of working memory clear-out [ 13 ]. Whether such dynamics can be observed in human electrophysiology and how these neural dynamics change with age is unknown.

We examined the neural mechanisms underlying age-related decline in multiple working memory phases. To accommodate the increased interindividual variability in cognitive aging [ 2 , 15 ], we were further interested in studying neural metrics that are capable of characterizing individual differences in both younger and older adults. Neural variability is an understudied property of brain dynamics, which is increasingly recognized as a sensitive index capable of tracking intra- and interindividual brain–behavior relationships [ 16 – 18 ]. It reflects the joint influence of sensory input, arousal state, attention, and high-order demand variations on brain functions [ 18 ]. In particular, behavioral relevance of cross-trial variability has been reported in multiple research fields, with lower variability associated with superior perception [ 19 ], more internally guided decision-making [ 20 ], and less social conformity behavior [ 21 ]. Thus, we leveraged cross-trial neural variability to examine the beta-band oscillatory dynamics during maintenance and after response, with a particular focus on age-related differences.

Pronounced age-related working memory deficits with increasing set size

Twenty younger (22.1 ± 2.5 years) and 21 older (70.6 ± 4.8 years) adults performed a delayed match-to-sample task involving 1, 2, or 4 sequentially presented real-world objects during concurrent electroencephalography (EEG) ( Fig 1A ). Participants were instructed to indicate using a corresponding button press whether a subsequently presented probe item was identical to any one of their memory representations. Feedback was presented 0.5 s after response. Behavioral performance accuracy was better in younger than older participants ( Fig 1B , F(1, 39) = 5.572, p = 0.023, partial χ 2 = 0.125, BF 10 = 2.275), at lower compared to higher set sizes (F(2, 78) = 160.548, p < 0.001, partial χ 2 = 0.805, BF 10 > 100). There was a significant interaction between set size and age group (F(2, 78) = 8.458, p < 0.001, partial χ 2 = 0.178, BF 10 = 59.334), which was driven by larger age-related working memory deficits at set size 2 and 4 (set size 1, F(1, 39) = 0.47, p = 0.497; set size 2, F(1, 39) = 6.18, p = 0.017; set size 4, F(1, 39) = 10.39, p = 0.003). Reaction time (RT) increased with set size ( Fig 1B ; F(2, 78) = 52.359, p < 0.001, partial χ 2 = 0.573, BF 10 > 100) and older participants were slower than younger participants (F(1, 39) = 8.788, p = 0.005, partial χ 2 = 0.184, BF 10 = 7.011). There was no age × set size interaction on RT (F(2, 78) = 1.655, p = 0.198, BF 10 = 0.299).

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( A ) Delayed match-to-sample task. One, 2, or 4 images were presented sequentially. Intertrial interval was jittered from a uniform distribution (1.2 to 1.6 s). ( B ) Behavioral results. There is a pronounced age-related memory accuracy decrease at higher loads. No significant age × set size interaction effect was observed in reaction time. Lighter and darker colors represent younger and older adults, respectively. Error bars show standard error of the mean. Circled dots show individual participant data. * p < 0.05, ** p < 0.01. Source data can be found at https://doi.org/10.5281/zenodo.12735828 ( S1 Data ).

https://doi.org/10.1371/journal.pbio.3002784.g001

Beta variability tracks brain dynamics as hypothesized by cognitive neurophysiological theory of aging

Changes of rhythmic activity at the trial level lead to alterations in trial-averaged power and cross-trial variability ( Materials and methods , S1A Fig ). Rhythmic activity, unless stated otherwise, was measured using cross-trial variability, which captures fluctuations unique to each individual. Cross-trial beta band variability captured the load-dependent neurophysiological changes in younger and older adults predicted by the Compensation-Related Utilization of Neural Circuits Hypothesis (CRUNCH) [ 22 ]. CRUNCH posits that older adults would not show a parametric neural change with memory load increases during maintenance. This is because older adults overrecruit resources at low set sizes resulting in a resource shortage when set size further increases. We selected the frontal and centroparietal clusters as the channels of interest due to their relevance to working memory function as revealed in previous studies [ 23 , 24 ].

When examining beta variability of each set size and age group during the maintenance phase, we observed an interaction between age group and set size in the frontal cluster alone ( Fig 2A , F(2, 117) = 3.886, p = 0.023, partial χ 2 = 0.087, BF 10 = 40.957; see S1 Table for centroparietal cluster). This suggests that age differentially influences how beta variability changes with memory load. To further quantify this critical interaction effect, we performed linear regression on the beta variability across the 3 set sizes for each participant and tested the slope of their best fit lines at the population level. The parametric variability change with increasing memory load was significant in younger (mean slope = −0.073, t(19) = 3.642, p = 0.002, Cohen’s d = 0.814, two-tailed), but not older participants (mean slope = −0.024, t(20) = 1.474, p = 0.156). In other words, while there was a load-dependent variability decrease in younger adults, older adults failed to show such a systematic modulation. A closer examination of the beta variability modulation pattern in older adults suggests an inability to further modulate beta variability when the memory load increased from 2 to 4 (t(20) = 0.674, p bonferroni > 1.000), echoing predictions from CRUNCH. Analyses of the encoding phase did not reveal any significant differences between younger and older adults, ruling out the possibility that the observed group differences in load-dependent changes during maintenance stemmed from the encoding phase ( S1 Fig ). Together, these analyses suggest that beta-band dynamics during maintenance capture the fundamental premises of CRUNCH, and cross-trial beta variability is equipped with the sensitivity for investigating mechanistic differences in memory processes between younger and older adults.

( A ) Averaged beta-band variability at frontal sites during maintenance (0 to 3 s) and post-response (0.1 to 0.5 s). Inserted panel shows mean slope of load-dependent beta variability during maintenance and main effect of set size during post-response. Gray lines show individual data. ( B ) Maintenance beta variability predicts younger adults’ working memory accuracy. The behavioral relevance of maintenance beta variability was weak at load 1 (Younger: Rho pearson = 0.202, p = 0.392; Older: Rho pearson = 0.287, p = 0.202), suggesting that maintenance-related activity was not behaviorally predictive when the task was less demanding. ( C ) Post-response beta variability at the frontal and centroparietal clusters predicts older adults’ memory accuracy. For frontal beta variability, there was an outlier in the older group that showed high increase of beta variability. Excluding the outlier did not change the statistical significance of the correlation between post-response variability and memory accuracy (B = 0.055, p = 0.004, R 2 = 0.072). Shaded regions represent 95% confidence intervals. R 2 represents variance explained by maintenance or post-response beta variability. Source data can be found at https://doi.org/10.5281/zenodo.12735828 ( S2 Data ).

https://doi.org/10.1371/journal.pbio.3002784.g002

Beta variability during the maintenance phase predicts working memory performance exclusively for younger adults

Since variability-based measures are deemed superior for detecting interindividual differences [ 18 ], we leveraged cross-trial variability to assess more fine-grained differences between age groups. To examine whether beta variability during maintenance predicts individual memory performance and whether such an association presents differently between the age groups, we performed a generalized linear mixed model (GLMM), comparing the effect of frontal beta variability estimated across trials of each set size on memory accuracy between younger and older adults (see Materials and methods ). We observed a significant interaction of age group and beta variability on memory performance (F(1, 119) = 4.330, p = 0.040, partial χ 2 = 0.035, BF 10 = 8.545). This suggests a differential relationship between maintenance beta variability and memory performance in younger and older adults. Further analysis revealed that younger participants with lower variability during maintenance performed better ( Fig 2B ; B = −0.075, p = 0.025, R 2 = 0.060), especially when examining set size 2 and 4 (B = −0.137, p = 0.001, R 2 = 0.218). In contrast, beta variability during maintenance failed to predict memory performance for older adults (B = 0.003, p = 0.905). This implies that beta variability during the maintenance phase not only showed load-dependent changes at the population level but also predicted interindividual differences in memory performance selectively for younger adults. Aging, on the other hand, appeared to impede these systematic modulations to an extent that behavioral relevance of interindividual beta variability was no longer evident in older adults. The absence of maintenance-related activity in predicting older adults’ performance suggests that, while maintenance is influenced by aging (as evident in CRUNCH-like observations reported above), it may not be the primary working memory processing component that predicts behavioral differences in older adults at the individual level. In light of this finding, we investigated age-related differences beyond the maintenance phase.

Beta variability during the post-response phase predicts working memory performance exclusively for older adults

Given that maintenance-related beta variability could not track interindividual differences in older adults, we hypothesized that the post-response phase may capture such differences. This hypothesis was derived from 2 premises. One, the inhibition deficit theory implicates deletion deficits to be the primary driver of age-related memory decline [ 8 ]. Two, beta rhythmic dynamics post-response, particularly originating from where memory representations are maintained, have been interpreted as the neurophysiological signal of memory deletion [ 13 , 25 ]. Together, these premises link post-response beta rhythms with working memory deficits in aging. To test this hypothesis, we first examined whether frontal beta variability showed systematic changes with set size post-response (0.1 to 0.5 s). Unlike the maintenance phase where such a systematic change was evident only in younger adults, we found that beta variability significantly reduced with increasing set sizes for both younger and older adults ( Fig 2A ; F(2, 117) = 4.852, p = 0.009, partial χ 2 = 0.133, BF 10 = 98.128), with no significant difference between groups (F(1, 117) = 0.319, p = 0.573, BF 10 = 1.172) or age × set size interaction effect (F(2, 117) = 0.658, p = 0.520, BF 10 = 1.921). These findings suggest that post-response beta dynamics continue to be associated with working memory function despite aging.

Next, we examined whether post-response beta variability predicted individual memory performance of each set size, using a similar GLMM as was performed for the maintenance phase. We found a surprising reversal of patterns relative to those observed during the maintenance phase. Post-response frontal beta variability correlated with memory performance in older but not younger adults ( Fig 2C ; Younger: B = 0.005, p = 0.847; Older: B = 0.040, p < 0.001, R 2 = 0.094; age × beta variability interaction, F(1, 116) = 4.155, p = 0.044, partial χ 2 = 0.034, BF 10 = 5.972). In other words, while individuals’ memory performance for younger adults was driven primarily by frontal beta variability during maintenance, it was the post-response variability that determined memory performance for older adults. A similar pattern was observed for centroparietal channels (age group × beta variability, F(1,119) = 16.736, p < 0.001, partial χ 2 = 0.127, BF 10 > 100; Older: B = 0.057, p < 0.001, R 2 = 0.186; Younger: B = −0.035, p = 0.086). Given the temporal progression between maintenance and post-response phases, one may consider the individual correlation results in older adults as a later manifestation of a maintenance-related effect, perhaps due to overall slowing of information processing with aging [ 26 ]. However, this possibility is ruled out when examining the direction of the association between beta variability and memory performance. While reduced variability during maintenance predicted better performance for younger adults, it was increased variability during the post-response phase that predicted better performance for older adults. The dynamic change of beta variability during maintenance and post-response matches observations from previous research [ 13 ]. Our findings suggest that older and younger adults differentially leverage beta dynamics during distinct working memory processes to optimize their memory performance. Regulating beta variability during maintenance benefits younger adults but with aging, and perhaps due to the structural and functional reorganization that accompanies it [ 27 ], the neurophysiological locus predicting interindividual working memory differences in older adults shifts to the post-response phase.

With the post-response phase emerging as the possible locus of memory predictive activity in older adults and previous evidence for marked functional reorganization with aging [ 27 ], we examined brain networks recruited during the post-response phase, which may underlie the different contributions of beta variability between younger and older adults. Younger adults, whose memory performance was not associated with post-response beta variability at the individual level, recruited a widely distributed network spanning frontal and centroparietal brain regions ( Figs 3A and S2 ) [ 10 ]. On the other hand, older adults showed, on average, a spatially restricted network confined to centroparietal regions with a marked absence of frontal engagement. We speculated that the sensitivity of beta variability to individual memory performance in older adults may be related to the extent to which an older individual is able to recruit the frontal cortex during the post-response phase. Indeed, the individual with the best memory performance exhibited pronounced increases in beta variability in frontal regions relative to the participant with median memory accuracy. In contrast, the individual with the lowest memory accuracy did not show any increase in beta variability in any region. While we consider these results qualitative and preliminary, they suggest that the deficient ability to involve the frontal beta rhythms may lead to memory decline with aging. This also contributes to recognizing post-response beta variability as the sensitive index that tracks interindividual differences in older adults.

( A ) Source reconstruction of post-response beta variability. Top panel shows cluster-based permutation t -values ( p < 0.005) when comparing post-response beta increase relative to pre-response (−0.4 to −0.1 s). Exemplars represent older participants in the 99%, 50%, and 1% percentile of the present sample based on averaged memory accuracy. ( B ) Single-trial analysis. Stronger frontal beta activity benefits memory accuracy of the next trial. Violin plot shows the distribution of trial-wise post-response beta power of trial n-1. Red crosses represent the mean. Line plot shows the averaged data. Error bars represent standard error of the mean. ( C ) Correct trials showed larger post-response beta variability increase. Colored dots represent individual participant data. Source data can be found at https://doi.org/10.5281/zenodo.12735828 ( S3 Data ).

https://doi.org/10.1371/journal.pbio.3002784.g003

Post-response beta dynamics likely index information deletion: Evidence from 2 converging analyses

So far, we have observed a dissociation in the working memory phase where cross-trial variability in the beta band predicts memory performance in younger and older adults.

As previously mentioned, changes in post-response beta activity, indexed by beta bursting or beta power, have been understood as a signature of information deletion [ 13 , 28 ]. Since cross-trial variability is computed using power data from single trials, it is thus possible that cross-trial beta variability also characterizes the deletion process. Indeed, we found power changes at the single-trial level were associated with changes in cross-trial variability ( S1A Fig ), suggesting power and variability changes likely arise from the same cognitive process in this context. To explore this possibility, it is important to verify whether beta activity measured in any form provides collective evidence of information deletion. To this end, we examined how single-trial power, upon which cross-trial variability is computed, impacted memory performance. Then, we extended this analysis and directly examined the association between cross-trial variability and memory performance.

First, we speculated that post-response beta power in each trial should influence the accuracy of the next trial. Specifically, if post-response beta increase indexes deletion of memory representations, then stronger beta power at the single-trial level should benefit performance in the next trial. Consequently, there should be a positive correlation between post-response beta power of the current trial and memory performance of the next trial (brain-to-behavior relation, an “N+1 correlation”). Indeed, larger post-response beta power increase in the previous trial facilitated performance of the current trial exclusively for older adults ( Fig 3B ; Older: t(8832) = 5.662, p < 0.001, Cohen’s d = 0.175; Younger: t(9053) = 0.586, p = 0.557, permutation t test, two-tailed, alpha = 0.001; age groups × post-response beta power of previous trial, F(1, 17817) = 4.296, p = 0.038, log odds ratio = 0.458), see S2 Table and S3 Fig ). This pattern of results fits the deletion account, suggesting that a stronger increase in beta power post-response potentially frees up the capacity-limited working memory, thereby benefitting the next trial’s performance.

The preceding analysis rests on power changes in single trials. Does cross-trial variability computed using single-trial power also show evidence in favor of the deletion account? If beta variability also indexes deletion, then the extent to which variability is modulated in the post-response phase might be determined by the strength of the memory representations. When memory representations are weak or partial such that they produce an erroneous response, less deletion will be required on those incorrect trials relative to correct trials. Thus, post-response beta variability should be smaller on incorrect trials relative to correct trials irrespective of age group (behavior-to-brain relation). This was indeed the case. Incorrect trials showed lower frontal beta variability than correct trials ( Fig 3C ; F(1, 38) = 7.052, p = 0.012, partial χ 2 = 0.157, BF 10 = 3.923). These effects were consistent across both younger and older participants, with no significant difference between age groups (F(1, 38) = 0.615, p = 0.438, BF 10 = 0.451), and no interaction effect of age and correctness (F(1, 38) = 2.106, p = 0.155, BF 10 = 0.794). Thus, post-response frontal beta dynamics were more pronounced in correct trials where the strength of memory representations was stronger and require more deletion.

Both analyses provide converging findings. The behavioral association of single-trial beta power and cross-trial variability fits the predictions of the deletion account of beta rhythms. This provides confidence in our understanding that the specific prediction of interindividual differences in memory performance of older adults during the post-response phase by variability in the beta band likely reflects a facet of the deletion process.

Control analyses: Ruling out alternative explanations for the post-response beta increase

We have excluded several competing explanations of the post-response beta effect. First, the increase in post-response beta variability should not be interpreted as a reflection of error monitoring. Stronger monitoring is typically reported in incorrect trials compared to correct trials [ 29 ]. However, we observed the opposite trend in post-response beta variability, with a more substantial increase found in correct rather than incorrect trials. Moreover, error monitoring signals have typically involved lower frequency activity in the theta band [ 30 ], unlike the beta-band frequency under consideration here. As such, beta variability increase is a poor candidate for post-response error monitoring.

Second, increase in post-response beta activity could be interpreted as a feedforward confidence estimation process [ 31 ], which plausibly explains the stronger beta variability increase on correct trials relative to incorrect trials, overall. To address this possibility, we again leveraged single-trial beta power upon which variability measures are based. This time, however, we examined the behavioral correlation of single-trial beta power on current trial performance as done in previous studies [ 31 ]. Specifically, if beta power indexes confidence, then it should positively correlate with memory performance in the same trial (an N-N correlation). However, this was not the case. Behavioral performance of the current trial could not be explained by post-response beta activity at the trial level (accuracy: F(1, 17877) = 1.276, p = 0.259; RT: F(1, 15602) = 0.894, p = 0.345), and current trial’s post-response beta power was not modulated by accuracy (F(1, 17877) = 1.044, p = 0.307) or RT (F(1, 15602) = 1.844, p = 0.174). Thus, we do not consider confidence-related processing, characterized by post-response beta power increase, to be a compelling explanation in this case. Consequently, we do not consider changes in variability, stemming from power changes, to be reflective of the confidence estimation process either.

Lastly, there is a possibility that the observed N+1 correlation reflects the implementation of a preparatory attentional state rather than deletion of previously held memory representations. However, attentional deployment is typically associated with alpha-band activity [ 32 , 33 ]. Control analyses on post-response alpha activity did not show any significant effects (ps > 0.168). In addition, previous literature suggests that desynchronization of alpha rhythms benefits next trial performance [ 32 ]. The direction of this association is opposite in our data where it is enhanced power and variability in the beta band that are reflective of better performance in the following trials. Moreover, given that feedback was presented after responses, preparatory attention in service of the next trial is unlikely to be implemented until feedback offset and the onset of the intertrial interval.

Taken together, we believe that the deletion account of the increased post-response beta power and variability reflects a more coherent and parsimonious explanation than other accompanying cognitive processes during the same information processing window.

Control analyses: Spectral specificity of the dissociable effects

The dissociable effects we observed as to which memory processing phase contributes to individual performance differences in younger and older adults were specific to the beta band. The critical interaction between age and set size during the maintenance phase was not significant at any other frequency outside the beta band ( S1 Table ; ps > 0.126). We further examined other frequencies in the post-response phase. Again, we did not observe any significant differences between younger and older adults outside the beta band (GLMM age × variability, ps > 0.108). Moreover, the differences we observed during both maintenance and post-response phases could not be explained by signal quality, as there was no significant between-group difference in signal-to-noise ratio (SNR) in any frequency band (ps > 0.101). Thus, differences in how cross-trial variability predicts performance during separate working memory stages in younger versus older adults are spectrally specific to the beta band and cannot be explained by nonspecific changes in the EEG signal.

Control analyses: Trial-averaged power modulations

Since cross-trial variability is computed using measures of power, and since cross-trial variability results align with those observed using single-trial power data (for instance, N+1 correlations above), we asked whether examining variability provides any additional benefits over and above the examination of conventional trial-averaged power measures alone. It turned out that trial-averaged power failed to capture several significant observations evident through the examination of variability. First, trial-averaged beta power did not show the critical age × set size interaction during maintenance (F(2, 117) = 2.009, p = 0.137). Moreover, trial-averaged frontal beta power did not reflect the dissociable interindividual correlation between maintenance-related beta activity and memory performance (Younger: B = −0.022, p = 0.316; Older: B = −0.002, p = 0.912). Analyses of the trial-averaged post-response spectral activity failed to establish the frequency-specific behavioral relevance of post-response beta activity. Despite the significant correlation between older adults’ memory accuracy and trial-averaged frontal beta power (Older: B = 0.022, p < 0.001, R 2 = 0.117), this nonspecific correlation was also observed in the delta band (Older: B = −0.015, p = 0.038, R 2 = 0.038), alpha band (Older: B = 0.018, p = 0.007, R 2 = 0.062), and gamma band (Older: B = 0.030, p = 0.044, R 2 = 0.036). Post-response beta-band SNR showed no significant difference between age groups (F(1, 39) = 0.042, p = 0.838), neither did the trial-averaged beta power (F(1, 39) = 0.052, p = 0.820). Of note, the absence of age differences in SNR or trial-averaged power during post-response phase suggests that our results should not be explained by general differences between younger and older adults in the robustness of evoked neural responses to events, which could potentially mark a relevant boundary in a trial. The presence of a significant N+1 correlation using single-trial power together with the absence of age-related maintenance effects and the absence of spectral specificity of post-response beta activity using trial-averaged power imply that trial-wise fluctuations are canceled out in the trial-averaging approach. Thus, cross-trial variability, rather than trial-averaged power, in the beta band, appears to be a trait-like signature that is more sensitive to age-related differences in distinct working memory functions. This agrees with prevailing ideas about the greater sensitivity of variability-based measures in capturing interindividual differences [ 17 – 19 ].

Working memory function is a critical cognitive ability that deteriorates with age following adulthood [ 34 ]. But whether different processes within working memory are differentially affected by age remains understudied. This effort is further complicated by the fact that the degree of deterioration is variable across people [ 2 ]. Explaining the neurophysiology of working memory decline in aging requires us to examine the constituent processes within working memory simultaneously and consider variability at the interindividual and intraindividual levels as a parameterized function to be explained, rather than mere noise [ 16 ]. To this end, we adopted a novel approach to assess between- and within-group differences across ages. We combined cross-trial variability, which has largely been studied with broad-band EEG signal and fMRI hemodynamic responses [ 16 , 17 , 19 ], with rhythmic dynamics in the beta range, and examined them during both working memory maintenance and post-response deletion phases. Our novel analytical approach suggests that, when considering cross-trial fluctuations of beta power, variability explains individual differences in working memory performance during distinct phases for each age group. Whereas individual memory performance of younger adults was explained by frontal beta variability during maintenance, memory performance of older adults was primarily explained by post-response beta variability. Thus, task-related cross-trial variability augments individual state-dependent characteristics and predicts behavioral differences within and across age groups. With the age-related dissociations between maintenance and post-response phases, beta variability may serve as an age-related, task-sensitive signature of individual differences in distinctive working memory computations.

When developing models of age-related decline in working memory, it is imperative to incorporate the cognitive and neural dynamics during each information processing state. Most theories of aging are coarse-grained at the cognitive level of analysis [ 26 ], with little to say regarding distinct information processing phases. As an example, CRUNCH offers a plausible explanation for the pattern of neural effects during maintenance but does not directly address differences in post-response deletion. Our findings provide some relevant insights. For instance, the pattern of results during the post-response stage in the present study suggests impairments in information deletion and a putative inability to recruit compensatory resources. Specifically, CRUNCH would predict the involvement of additional neural processes for rescuing impaired information deletion. This should result in the characteristically saturated neural response pattern with increasing set sizes during the post-response phase, as we observed during the maintenance phase. However, we found consistent positive correlations between post-response beta variability and memory performance across all set sizes. CRUNCH would also predict an overactivation of frontal beta activity or additional engagement of task irrelevant regions during post-response phase to achieve efficient deletion. In contrast, our preliminary source estimation analyses suggest an underrecruitment of frontal regions during the post-response phase. These findings align with the inhibition deficit theory but suggest that compensatory resources, as posited by CRUNCH, could not be instantiated by older adults at least in the present investigation. Perhaps the inability to remove information efficiently during the deletion phase creates a bottleneck. This bottleneck could then influence memory maintenance in the following trial, where compensatory mechanisms during maintenance can still be called upon. In this manner, inefficient information deletion may be one of the reasons for the engagement of compensatory mechanisms during the maintenance phase. By viewing working memory as an information processing system that needs to be continuously regulated, we may be able to bridge the inhibition deficit theory with CRUNCH, through examination of the interdependent nature of information removal and maintenance, as demonstrated in the present study.

We interpret the change in post-response beta dynamics as a reflection of a memory deletion process in agreement with previous studies [ 13 , 28 ]. This interpretation is further supported by the observation of single-trial post-response beta power influencing the memory performance in the next trial. It is possible that changes in single-trial beta power are indices of memory deletion, with cross-trial variability, computed using single-trial power measures, reflecting a trait-like ability to execute and adjust the deletion process when memory needs to be regulated rapidly over trials with varying memory loads. We further think that the overall pattern of results sets the stage for elucidating the nature of the deletion process with greater functional specificity. For instance, it is possible that the increases in post-response beta power and variability signify the demand for deletion (the demand account). This account hypothesizes that stronger beta engagement reflects the absolute amount of information to be deleted. In other words, a stronger increase in trial-wise post-response beta power would reflect a stronger demand for deletion. And since cross-trial variability is computed from, and associated with single-trial power ( S1A Fig ), this relationship may be evident with cross-trial variability also. As a result, this account would expect a larger increase in beta variability with increasing set size. For instance, it is possible that the increase in post-response beta power signifies the demand for deletion. This account hypothesizes that stronger beta engagement (both in terms of single-trial power and cross-trial variability) reflects the absolute amount of information to be deleted. Therefore, this account would expect a larger increase in beta variability with increasing set size. This was not the case in our data where we observed a decrease in post-response beta variability with increasing set size ( Fig 2A , right). The overall pattern of results can be better explained if we consider beta engagement as a reflection of the efficiency of the deletion process (the efficiency account). The efficiency of deletion may be a composite of the total amount of information to be removed, the state and strength of to-be-deleted memory representations, the time available for the deletion process, and the rate at which information can be deleted. Given a fixed period of time available for uninterrupted deletion (500 ms in the present work), a smaller proportion of information could be removed when the total amount of information to be removed is higher (for instance, load 4) than lower (for instance, load 1). The negative association between beta variability and set size might suggest that a smaller proportion of to-be-removed information has been removed in the window of analysis at higher set sizes compared with lower set sizes. To the extent that information can be removed more efficiently within the same time window, deletion will be facilitated and performance on the next trial will benefit. This explains why we observe the N+1 correlations whereby a stronger beta power in the present trial benefits performance on the following trial, even though, overall, the efficiency of the deletion process may be lower at set size 4 given a limited deletion period. New experiments are needed to test these novel interpretations of beta dynamics. Mapping out the deletion dynamics over post-response periods of different lengths [ 35 ] and various memory loads [ 10 ] may be a good starting point.

The present findings set the stage for multiple future investigations. For example, it would be prudent to examine the role of object familiarity in the integrity of maintenance and deletion processes. Older people may exhibit some differences in their ability to recall and name objects [ 36 – 38 ], which could at least partially contribute to some memory differences in the present findings. Replicating the study while measuring levels of familiarity and comparing performance with conditions involving abstract, nonnameable objects may be one such approach. It would also help to replicate the findings with larger sample sizes. While the present study was adequately powered to detect an interaction effect between age and set size on memory performance, the interindividual correlations emerging from the present investigation can now be subjected to further follow-up investigations with a larger sample size. In addition, given recent reports suggesting changes in instantaneous beta frequency on a trial-by-trial basis [ 39 ], granular investigations on the relationship between deletion and trial-wise or individualized peak frequencies can be implemented. Our findings also hold implications for cognitive processes beyond those being investigated in the present study. For instance, it would be interesting to see whether the beta rhythmic dynamics facilitating deletion in the present study also contribute to other regulatory processes such as deprioritization [ 40 ], directed forgetting [ 41 ], or controlled removal operations such as information suppression or replacement [ 6 ]. Moreover, whether similar neural mechanisms contribute to the transfer of information between working memory and long-term memory needs to be investigated, for it may hold the key to understanding how representations of our continuous experience in working memory are transformed into discrete, segmented representations in long-term memory [ 42 , 43 ]. Finally, future research is needed to test the causal role of beta activity in modulating the influence from deficient deletion to subsequent maintenance in older adults. It may turn out, as our results showing age-related dissociations between maintenance and post-response indicate, that working memory in younger and older adults may have distinct influences of different neural mechanisms in influencing memory performance, suggesting new directions for future model building, and, ultimately, a more comprehensive mechanistic understanding of cognitive aging in health and disease.

Materials and methods

Ethics statement.

The study protocol was reviewed and approved by the Boston University Institutional Review Board (IRB number 4230E). The research adhered to the ethical guidelines outlined in the Declaration of Helsinki. Written informed consent was obtained from all participants. Participants were compensated $15 per hour.

Participants

Power analysis (80% power, p = 0.05; repeated measures for the critical age × set size interaction) on pilot data ( n = 10) showed a Cohen’s f effect size of 0.248 for the interaction effect, indicating that a total sample size of 28 participants (14 participants per group) would be sufficient to reliably capture an effect. To account for dropouts and exceed these minimum power calculations, we sought at least 20 participants per age group. Twenty-two younger adults and 21 older adults from the greater Boston metropolitan area enrolled in the study. All older participants were prescreened either via phone or an online questionnaire to ensure study eligibility on the following criteria: (1) normal or corrected-to-normal vision and hearing; (2) fluent English speaker; (3) no history of neurological problems or head injury; (4) never been knocked unconscious for longer than 10 minutes; (5) not currently pregnant during the time of study participation; (6) no metal implanted in the head; (7) no implanted electronic devices (pacemaker, neurostimulator); (8) no formal diagnosis of severe tinnitus; (9) no formal diagnosis of a substance problem (related to alcohol or drugs of any kind). Two younger participants’ data were excluded from the analysis due to excessive eye blinks (>60% trials removed in preprocessing). The final sample consisted of 20 younger participants aged 19 to 27 years old (22.1 ± 2.5 years, 10 females, education years 16.0 ± 2.3) and 21 older participants aged 60 to 81 years old (70.6 ± 4.8 years, 7 females, education years 17.3 ± 2.9). Three older participants performed 20 blocks, 23 blocks, and 26 blocks out of a total of 30 blocks due to fatigue.

Behavioral task

We used object stimuli from a previous study [ 44 ]. One, 2, or 4 images of objects were presented sequentially in each trial. Each object was presented for 200 ms followed by an interstimulus interval (ISI) of 1,000 ms. Once all stimuli were presented, the fixation cross turned green. After a delay of 3,000 ms, a probe image was presented for 200 ms, and participants were asked to determine whether the probed image was either identical (50%) or different (50%) from the previous images, by pressing one of 2 buttons on a handheld gamepad. Participants had unlimited time to respond but were instructed to respond as quickly and accurately as possible. Feedback was presented after 500 ms of the response, in the form of a colored circle for 1,000 ms. Yellow indicated a correct response and blue indicated an incorrect response. Color mapping was counterbalanced across participants. New trials began after a jittered time of 1,200 to 1,600 ms (uniform distribution). There were 30 blocks, each containing 24 trials with mixed set sizes, resulting in a total of 720 trials evenly divided among the 3 different set sizes. Participants completed multiple practice blocks until they understood the instruction, showed an averaged accuracy above 0.8, and felt comfortable proceeding.

Electroencephalography

EEG was recorded at a sampling rate of 1,000 Hz in a dimly lit EEG booth using 64 Ag/AgCl electrodes mounted in a BrainCap elastic cap according to the international 10–20 system. The right mastoid electrode served as the online reference. Data were online bandpass filtered to 0.01 to 125 Hz. Four EOG channels were placed at the outer canthi of each eye, above and below the left eye. Impedance levels were kept below 10 kOhm. Participants were instructed to fixate on the central cross throughout each trial, minimize eye blinks and facial movements, and remain still during each block.

EEG preprocessing

EEG preprocessing and analysis were conducted using custom Matlab scripts with the EEGLAB [ 45 ] and Fieldtrip [ 46 ] toolboxes. Raw EEG data were bandpass filtered from 0.5 to 40 Hz and re-referenced to the average of both mastoids. Broken channels were interpolated using a spherical spline method (EEGLAB function, “pop_interp(‘spherical’)”). We extracted epochs time-locked to delay onset (−5.7 to 6 s) and performed independent component analysis (ICA) to correct artifacts caused by eye movements, blinks, heart, muscle, and line noise. The number of removed components was slightly more in older than younger participants (Older: 7.6 ± 2.9; Younger: 6.2 ± 1.6; t(39) = 1.926, p = 0.061). We also removed trials with noisy data points that exceeded a voltage threshold of 100 μV. Improbable and abnormally distributed data points beyond 8 standard deviations of the mean probability distribution and kurtosis distribution were also removed. After preprocessing steps, there were 79.4 ±10.2% clean trials from younger adults and 80.0 ± 9.3% from older adults. Additional segmentation was performed depending on time periods of interest with 0 to 3 s for maintenance and 0.1 to 0.5 s post-response phases.

Behavioral analyses

Mean RT was calculated based on correct trials. Trials with RTs slower than 5 s, or beyond 3 standard deviations of individuals’ own mean RT were excluded from averaging. Two-way mixed Omnibus ANOVA was conducted with the between-participants factor of group (younger versus older) and the within-participants factor of set size (1 versus 2 versus 4). Bonferroni correction was applied for multiple comparisons. Bayesian ANOVA was performed using JASP 0.17.3.

Time-frequency decomposition

For each trial, we subtracted the averaged waveform of each set size to remove phase-locked activity. Single-trial EEG spectral decomposition was then performed for frequencies ranging from 1 to 40 Hz (1 Hz steps) using Morlet wavelets (width linearly increased from 2 to 10) with a time window of 50 ms. Baseline normalization was performed using decibel conversion relative to the pre-trial (−0.4 to −0.1 s) or pre-response period (−0.4 to −0.1 s) for maintenance and response-locked analysis, respectively. SNR was calculated at the averaged power (signal) divided by the standard deviation of the mean across trials (noise) during the interval 0 to 4 s relative to maintenance onset.

Variability analysis

To examine the load-dependent beta variability changes ( Fig 2A ), we performed linear regression on the beta variability across the 3 set sizes for each participant and tested the slope of their best fit lines versus zero at the population level. To assess the interindividual behavioral relevance of neural variability decrease, while controlling for the intraindividual load-dependent effect during maintenance and post-response ( Fig 2B and 2C ), GLMM was formulated as “averaged memory accuracy ~ age group * beta variability + (1 | set size)”. The intercept of each set size was added as the random effect because interindividual differences rather than intraindividual manipulation (set size effect here) is the primary focus. Moreover, we anticipated that individuals with lower variability during maintenance would show better memory performance across all set sizes. Averaged memory accuracy for each set size would theoretically range from 0 to 1 and the accuracy distribution in our case showed negative skewness. Thus, response distribution was specified as “Gamma” with inverse link function. We further confirmed the result by normalizing the response variable (Matlab function “betafit” and “betainv”). With the normalization, the link function was specified as “identity” and the response distribution was specified as “normal”. Regardless of the choices of link function and response distribution, results were consistent (maintenance: F(1, 119) = 3.806, p = 0.053, BF 10 = 6.473; post-response: F(1, 116) = 3.681, p = 0.058, BF 10 = 6.102). Regarding the significant interaction term between age group and beta variability, we constructed a regression model that included set size and beta variability as predictors separately for each age group. Similar analyses were performed on post-response frontal beta variability. An outlier in the older group showing a large post-response frontal beta increase ( Fig 2C ) was identified and excluded from the ANOVA analysis.

Comparison between correct and incorrect trials was performed on load-4 trials to obtain a sufficient number of incorrect trials ( Fig 3C ). Given the unbalanced proportion of correct and incorrect trials, we subsampled from correct trials to avoid bias arising from the unequal number of trials and iterated for 200 times. The averaged results of all iterations were used for statistical tests. An outlier was excluded from the Omnibus ANOVA analysis. Including the outlier did not alter the conclusion (F(1, 39) = 6.177, p = 0.017, partial χ 2 = 0.137, BF 10 = 3.016).

Single-trial analysis

N+1 analysis was performed to reveal the behavioral influence of post-response beta activity on the next trial. Since variability is an aggregated index across trials, we used trial-wise power for this analysis. Single-trial beta power during maintenance (0 to 3 s after stimulus onset) and post-response (0.1 to 0.5 s after response, with the first 100 ms removed to avoid motor artifacts and temporal smearing) were added into the GLMM model with the formula “Memory accuracy (n) ~ maintenance beta power (n) * set size (n) * post-response beta power (n-1) * set size (n-1) * age group + (1 + maintenance beta power + post-response beta power (n-1) + set size (n) + set size (n-1) | participant)”. The response distribution was specified as “Binomial” and the link function was specified as “logit”. We hypothesized that memory accuracy of the current trial was determined by set size and maintenance activity of the current trial, set size of the previous trial as well as the extent to which previous information was deleted. Participants’ intercept and slope variations of the fixed effect were added as the random term. Given the significant interaction effect between age group and post-response beta power of trial n-1, we separated trial n based on response accuracy and compared the post-response beta power of trial n-1 between correct and incorrect trials in each age group using a permutation t test ( Fig 3B , permutation times = 10,000, two-tailed, alpha = 0.001). The same analysis was conducted on within-trial variability calculated as the variance of normalized beta power during the maintenance and post-response phases.

N-N analysis was performed to investigate the relationship between post-response beta power and response confidence at the trial level. We examined whether the post-response beta power of the current trial could explain the same trial’s behavioral performance (“accuracy (n) or RT (n) ~ post-response beta power (n) * group * set size (n) + (1+ post-response beta power + set size (n) | participant)”). When using RT as the response index, the link function was specified as “inverse” for the gamma distribution. Only correct trials were included for the analysis of RT. When using accuracy as the response index, the link function was specified as “logit” for the binomial distribution. Additionally, we modeled the influence of response on post-response beta power (“post-response beta power (n) ~ RT (n) or accuracy (n) * group * set size (n) + (1+set size (n) + RT (n) or accuracy (n) | participant)”) with the link function specified as “identity” for the normal distribution.

Source localization

Linearly constrained minimum variance (LCMV) beamforming was used to reconstruct the cortical sources of post-response neural variability changes. Sensor-level data were referenced to the common average. A standard anatomical MRI and a boundary element method (BEM) headmodel from the Fieldtrip toolbox were used to construct a 3D template grid at 1 cm resolution in Montreal Neurological Institute template space. Given the distance between EEG electrodes and the scalp, we moved the brain surface 5 mm inwards from the skull to accommodate BEM stability (“ft_prepare_sourcemodel”). Channel neighborhood was defined using the default EEG template (“easycapM11_neighb.mat”). A common spatial filter was computed based on band-passed (15 to 25 Hz) EEG time series of all set sizes. Virtual channel time courses for all voxels were reconstructed separately for each set size using the common filter. We then performed time-frequency decomposition and calculated the relative variance on this virtual-channel data as we did on the sensor level. The leadfield comprises 4,050 grids. Source space cluster-based permutation tests were conducted using paired t tests on response relative to the pre-response baseline (−0.4 to −0.1 s). Monte Carlo nonparametric randomization was iterated for 10,000 times with the alpha level of the permutation test set to 0.005.

Supporting information

S1 fig. beta variability during encoding..

Related to Fig 2 . ( A ) Example data for load 1. Gray curves represent trial-wise beta power. The purple curve represents the average of all trials, and the green curve illustrates cross-trial variability. Cross-trial beta variability and trial-averaged beta power showed consistent patterns across time. They were correlated during maintenance (Load 1: Pearson’s Rho = 0. 841, p < 0.001; Load 2, Pearson’s Rho = 0.871, p < 0.001; Load 4, Pearson’s Rho = 0.683, p < 0.001) and post-response phases (Load 1: Pearson’s Rho = 0. 939, p < 0.001; Load 2, Pearson’s Rho = 0.951, p < 0.001; Load 4, Pearson’s Rho = 0.947, p < 0.001). Changes in single-trial beta power affect cross-trial variability. When we median-split beta power of each participant and compared the variance between subset of trials with higher and lower beta power, we found that trials with higher beta power had greater variance than those with lower beta power (maintenance: t(40) = 3.754, p < 0.001, Cohen’s d = 0.747; post-response: t(40) = 1.818, p = 0.038, Cohen’s d = 0.275, one-tailed t test). ( B ) Beta-band variability time course. Vertical dashed lines denote stimulus onsets. ( C ) Item-specific variability during encoding. Data were averaged from variability changes during each stimulus presentation (0 to 1.2 s). ( D ) Slope of variability changes. Boxplot shows the distribution of slopes. Slopes were obtained from linear fitting of beta variability changes induced by consecutive stimulus presentations in both load 2 and load 4. The central line represents the median, and black crosses represent outlier data points beyond 1.5 times the interquartile range. No significant age-related differences were observed in the slopes (Load 2: t(39) = 1.054, p = 0.298; Load 4: t(39) = 1.052, p = 0.299). Source data can be found at https://doi.org/10.5281/zenodo.12735828 ( S4 Data ).

https://doi.org/10.1371/journal.pbio.3002784.s001

S2 Fig. Cross-trial neural variability during maintenance and post-response.

Related to Figs 2 and 3 . ( A ) Time-frequency map of neural variability at frontal sites. ( B ) Frontal beta variability during maintenance phase. Time frequency (difference) map shows frontal neural variability averaged across set sizes. Topographical plots show the averaged beta-band variability (15 to 25 Hz) during the maintenance interval (0 to 3 s). Black dots on the topography highlight the frontal channels used to generate time-frequency maps. Shaded error bars on the time series represent the between-participant standard error. The colored vertical solid lines on the x-axis correspond to the mean RT of each set size. ( C ) Time-frequency (difference) map of post-response neural variability changes at the frontal site. ( D ) Topography of post-response (0.1 to 0.5 s) beta variability increases. The highlighted channels passed cluster-based permutation tests (alpha = 0.001, two-tailed).

https://doi.org/10.1371/journal.pbio.3002784.s002

S3 Fig. Related to Fig 3 .

The influence of post-response beta power of previous trials on memory accuracy. ( A ) The interaction effect among set size of the previous trial, post-response beta power of the previous trial, and the current trial’s set size. ( B ) The interaction effect of set size of previous trials, post-response beta power of the previous trial, and age groups. ( C ) The interaction effect among set size of previous trials, post-response beta power of the previous trial, current trial’s set size, and age groups. Error bars represent standard error of the mean. Source data can be found at https://doi.org/10.5281/zenodo.12735828 ( S3 and S5 Data).

https://doi.org/10.1371/journal.pbio.3002784.s003

S1 Table. Related to Fig 2 .

Analysis of maintenance-related activity. The critical interaction between age group and set size was observed in the beta band (15–25 Hz). The frequency band of interest was guided by existing literature; however, we also explored other frequency bands to demonstrate frequency specificity. While other spatiospectral combinations did not show significant age × set size interaction effects, we proceeded to separate age groups and examined interindividual correlation between maintenance activity and memory accuracy for completeness.

https://doi.org/10.1371/journal.pbio.3002784.s004

S2 Table. Related to Fig 3 .

GLMM output table of single-trial N+1 analysis using the formula “Accuracy (n) ~ set size (n) * maintenance beta power (n) * set size (n-1) * post-response beta power (n-1) * age group+ (1 + set size (n) + maintenance beta power (n) + set size (n-1) + post-response beta power (n-1) | participant)”. Participants’ accuracy of the current trial (n) is determined by set size and maintenance beta power of the current trial n, as well as set size and post-response beta power of the previous trial n-1.

https://doi.org/10.1371/journal.pbio.3002784.s005

S1 Data. Source data of Fig 1 .

https://doi.org/10.1371/journal.pbio.3002784.s006

S2 Data. Source data of Fig 2 .

https://doi.org/10.1371/journal.pbio.3002784.s007

S3 Data. Source data of Fig 3 .

https://doi.org/10.1371/journal.pbio.3002784.s008

S4 Data. Source data of S1 Fig .

https://doi.org/10.1371/journal.pbio.3002784.s009

S5 Data. Source data of S3 Fig .

https://doi.org/10.1371/journal.pbio.3002784.s010

Acknowledgments

We thank Phillip (Xin) Cheng for insightful discussion and Rutvi Jain for assistance with participant recruitment.

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• 8. Hasher L, Zacks RT, May CP. Inhibitory control, circadian arousal, and age. In: Gopher D, Koriat A, editors. Attention and performance XVII: Cognitive regulation of performance: Interaction of theory and application. The MIT Press; 1999. pp. 653–675.

Loftus Psychology: Pioneering Research in Memory and Eyewitness Testimony

Elizabeth Loftus, a trailblazing psychologist, has forever changed our understanding of the fragility and malleability of human memory, reshaping the landscape of cognitive psychology, eyewitness testimony, and legal proceedings. Her groundbreaking work has not only revolutionized our understanding of how memories are formed, stored, and retrieved but has also had far-reaching implications across various fields, from courtrooms to therapists’ offices.

Born in 1944, Elizabeth Loftus grew up in a time when psychology was still finding its footing as a scientific discipline. Little did anyone know that this curious young woman would go on to become one of the most influential psychologists of the 20th century. Her journey into the depths of human memory began with a simple question: How reliable are our recollections?

As a graduate student, Loftus was fascinated by the intricate workings of the human mind. She couldn’t help but wonder about the nature of our memories. Are they like videotapes, faithfully recording every detail of our experiences? Or are they more like clay, malleable and susceptible to change? These questions would drive her research for decades to come.

The Birth of False Memory Research

Loftus’ work on false memories is nothing short of revolutionary. It’s like she took a sledgehammer to our understanding of memory and rebuilt it from the ground up. Her research showed that memories aren’t just unreliable; they can be downright fabricated.

Imagine you’re at a party, and someone asks you about your childhood. You confidently recount a vivid memory of getting lost in a shopping mall when you were five. The fear, the tears, the relief when your mom finally found you – it all feels so real. But what if I told you that memory might be completely false?

This is exactly what Loftus demonstrated in her famous “Lost in the Mall” experiment. She showed that it’s possible to implant entirely false memories in people’s minds through suggestion and leading questions. Participants were given a booklet of childhood events, including a false story about getting lost in a mall. Surprisingly, about 25% of participants came to believe this false memory actually happened to them!

But Loftus didn’t stop there. She delved deeper into the concept of memory distortion in psychology , exploring how our recollections can be altered by post-event information. This phenomenon, known as the misinformation effect, has profound implications for eyewitness testimony and our understanding of autobiographical memory.

Of course, Loftus’ work wasn’t without controversy. Some critics argued that her research could be used to discredit genuine memories of trauma, particularly in cases of childhood abuse. But Loftus stood her ground, emphasizing the importance of scientific rigor in memory research.

Revolutionizing Eyewitness Testimony

Perhaps nowhere has Loftus’ work had a more tangible impact than in the realm of eyewitness testimony psychology . Her research has fundamentally changed how we view the reliability of eyewitness accounts in legal proceedings.

Picture this: You’re walking home late at night when you witness a crime. The police arrive, and you give your statement. You’re sure about what you saw – the color of the perpetrator’s jacket, the direction they ran, the weapon they used. But how reliable is your memory, really?

Loftus’ work suggests that eyewitness memories are far more fallible than we once believed. Through a series of clever experiments, she demonstrated how easily memories can be influenced by leading questions, suggestive interviewing techniques, and post-event information.

In one study, participants watched a video of a car accident. When asked, “How fast were the cars going when they smashed into each other?”, participants estimated higher speeds than when the verb “hit” was used instead. This simple change in wording affected not only their speed estimates but also their likelihood of reporting non-existent broken glass at the scene.

These findings have had profound implications for courtroom procedures. Thanks to Loftus’ work, many jurisdictions now provide specific jury instructions about the potential unreliability of eyewitness testimony. Interview techniques have been developed to minimize memory distortion, focusing on open-ended questions and avoiding suggestive language.

But the impact of Loftus’ work extends beyond the courtroom. It has also influenced our understanding of flashbulb memories in psychology , those vivid recollections of significant events that we once thought were immune to distortion.

Loftus Psychology in Clinical Settings

The ripple effects of Loftus’ research have reached far into clinical psychology, particularly in the realm of therapy and recovered memory techniques. Her work has sparked intense debate over the validity of repressed memories and their role in psychological treatment.

Imagine a patient who comes to therapy with vague feelings of unease and depression. Through guided imagery and hypnosis, they suddenly “recover” memories of childhood abuse. Before Loftus’ research, such recovered memories might have been taken at face value. But her work has shown that it’s possible to implant false memories, even of traumatic events, raising questions about the validity of such techniques.

This has led to a reevaluation of memory-focused therapeutic practices. Mental health professionals now have guidelines for addressing memory-related issues, emphasizing the need for caution when dealing with recovered memories. The focus has shifted from “uncovering” hidden memories to helping patients deal with their current symptoms and improving their quality of life.

However, this shift hasn’t been without controversy. Some therapists and patients argue that discrediting recovered memories could silence genuine victims of abuse. Loftus’ response? “I’m all for believing the children,” she once said, “but not if it means sending an innocent person to prison for life.”

Advancing Cognitive Psychology

While Loftus is perhaps best known for her work on false memories and eyewitness testimony, her contributions to cognitive psychology extend far beyond these areas. Her research has fundamentally changed our understanding of memory formation and retrieval.

Loftus’ work builds on the foundation laid by earlier memory researchers like Hermann Ebbinghaus , who pioneered the study of memory in the late 19th century. But where Ebbinghaus focused on the mechanics of memory – how quickly we forget, how repetition affects retention – Loftus delved into the subjective nature of memory.

Her research has shown that memory is not a passive recording of events, but an active, constructive process. Every time we recall a memory, we essentially reconstruct it, making it vulnerable to distortion. This understanding has implications not just for psychology, but for fields as diverse as education, marketing, and even artificial intelligence.

For instance, Loftus’ work on the role of suggestion in memory alteration has influenced educational practices. Teachers now understand the importance of careful wording when asking questions or providing feedback, to avoid inadvertently implanting false information.

Her research has also influenced other areas of psychological study. The concept of memory malleability has been applied to fields like decision-making psychology, social psychology, and even the study of autobiographical memory in psychology .

The Legacy and Future of Loftus Psychology

As we look to the future, the legacy of Elizabeth Loftus continues to shape the landscape of memory research. Her work has opened up new avenues of inquiry and sparked ongoing debates about the nature of memory and its role in our lives.

One exciting area of current research is the intersection of memory and technology. With the rise of digital media and virtual reality, researchers are exploring how these technologies might influence memory formation and recall. Could immersive VR experiences create more vivid (and potentially more distortable) memories? How might our increasing reliance on digital photos and social media affect our autobiographical memories?

Another frontier is the neuroscience of false memories. Advances in brain imaging techniques are allowing researchers to study the neural correlates of true and false memories, potentially shedding light on the mechanisms underlying memory distortion.

Loftus’ work has also had a profound impact on public awareness of memory issues. Her research has been featured in countless media reports, documentaries, and even popular TV shows, bringing complex psychological concepts to a wider audience.

As we grapple with issues like fake news, digital manipulation, and the blurring lines between reality and virtual experiences, Loftus’ insights into the malleability of memory are more relevant than ever. Her work reminds us to approach our memories – and those of others – with a healthy dose of skepticism and curiosity.

In conclusion, Elizabeth Loftus stands as a giant in the field of psychology, alongside other psychology pioneers who have shaped our understanding of the human mind. Her work on false memories and eyewitness testimony has not only advanced our scientific understanding but has also had real-world impacts in legal, clinical, and educational settings.

From the courtroom to the therapist’s office, from the classroom to the lab, Loftus’ insights continue to influence how we think about memory. Her legacy serves as a reminder of the power of rigorous scientific inquiry to challenge our assumptions and expand our understanding of the human mind.

As we move forward, the questions raised by Loftus’ work continue to drive research and debate. How can we balance the fallibility of memory with the need for justice in legal proceedings? How can we help people deal with traumatic memories without risking the creation of false ones? How does our understanding of memory malleability influence our sense of self and our relationships with others?

These are not just academic questions. They touch on fundamental aspects of human experience – our sense of identity, our understanding of truth, our ability to navigate the complex social world around us. As we continue to explore these issues, we owe a debt of gratitude to Elizabeth Loftus, whose pioneering work has forever changed how we understand the intricate, fallible, and endlessly fascinating realm of human memory.

References:

1. Loftus, E. F. (2005). Planting misinformation in the human mind: A 30-year investigation of the malleability of memory. Learning & Memory, 12(4), 361-366.

2. Loftus, E. F., & Pickrell, J. E. (1995). The formation of false memories. Psychiatric Annals, 25(12), 720-725.

3. Loftus, E. F. (1975). Leading questions and the eyewitness report. Cognitive Psychology, 7(4), 560-572.

4. Loftus, E. F., & Palmer, J. C. (1974). Reconstruction of automobile destruction: An example of the interaction between language and memory. Journal of Verbal Learning and Verbal Behavior, 13(5), 585-589.

5. Loftus, E. F. (2003). Make-believe memories. American Psychologist, 58(11), 867-873.

6. Schacter, D. L., & Loftus, E. F. (2013). Memory and law: What can cognitive neuroscience contribute? Nature Neuroscience, 16(2), 119-123.

7. Loftus, E. F. (2018). Eyewitness science and the legal system. Annual Review of Law and Social Science, 14, 1-10.

8. Howe, M. L., & Knott, L. M. (2015). The fallibility of memory in judicial processes: Lessons from the past and their modern consequences. Memory, 23(5), 633-656.

9. Patihis, L., Ho, L. Y., Tingen, I. W., Lilienfeld, S. O., & Loftus, E. F. (2014). Are the “memory wars” over? A scientist-practitioner gap in beliefs about repressed memory. Psychological Science, 25(2), 519-530.

10. Loftus, E. F. (2017). Eavesdropping on memory. Annual Review of Psychology, 68, 1-18.

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The Magical Mystery Four: How is Working Memory Capacity Limited, and Why?

Nelson cowan.

University of Missouri

Working memory storage capacity is important because cognitive tasks can be completed only with sufficient ability to hold information as it is processed. The ability to repeat information depends on task demands but can be distinguished from a more constant, underlying mechanism: a central memory store limited to 3 to 5 meaningful items in young adults. I will discuss why this central limit is important, how it can be observed, how it differs among individuals, and why it may occur.

It may not really be magical, but it is a mystery. There are severe limits in how much can be kept in mind at once (~3–5 items). When, how, and why does the limit occur?

In a famous paper humorously describing “the magical number seven plus or minus two,” Miller (1956) claimed to be persecuted by an integer. He demonstrated that one can repeat back a list of no more than about seven randomly ordered, meaningful items or chunks (which could be letters, digits, or words). Other research has yielded different results, though. Young adults can recall only 3 or 4 longer verbal chunks, such as idioms or short sentences ( Gilchrist, Cowan, & Naveh-Benjamin, 2008 ). Some have shrugged their shoulders, concluding that the limit “just depends” on details of the memory task. Recent research, however, indicates when and how the limit is predictable.

The recall limit is important because it measures what is termed working memory ( Baddeley & Hitch, 1974 ; Miller, Galanter, & Pribram, 1960 ), the few temporarily active thoughts. Working memory is used in mental tasks, such as language comprehension (for example, retaining ideas from early in a sentence to be combined with ideas later on), problem solving (in arithmetic, carrying a digit from the ones to the tens column while remembering the numbers), and planning (determining the best order in which to visit the bank, library, and grocery). Many studies indicate that working memory capacity varies among people, predicts individual differences in intellectual ability, and changes across the life span ( Cowan, 2005 ).

It has been difficult to determine the capacity limit of working memory because multiple mechanisms retain information. Considerable research suggests, for example, that one can retain about 2 seconds’ worth of speech through silent rehearsal ( Baddeley & Hitch, 1974 ). Working memory cannot be limited this way alone, though; in running span procedures, only the last 3 to 5 digits can be recalled (less than 2 seconds’ worth). In these procedure, the participant does not know when a list will end and, when it does, must recall several items from the end of the list ( Cowan, 2001 ).

UNDERSTANDING CENTRAL CAPACITY LIMITS

To understand the nature of working memory capacity limits, two distinctions matter. Whereas working memory ability is usually measured in a processing-related, inclusive way, it instead takes storage-specific, central measures to observe capacity limits that are similar across materials and tasks.

The processing-related versus storage-specific distinction has to do with whether one prevents processing strategies that individuals adopt to maximize performance, and whether one considers harmful processes that interfere with the best use of working memory. Storage-specific capacity is a more analytic concept and stays constant across a much wider variety of circumstances. In a broad sense, working memory ability varies widely depending on what processes can be applied to the task. To memorize verbal materials, one can try to repeat them in one’s mind (rehearse them covertly). One can also try to form chunks from multiple words. For example, to remember to buy bread, milk, and pepper, one can form an image of bread floating in peppery milk. To memorize a sequence of spatial locations, one can envision a pathway formed from the locations. Though we cannot yet make precise predictions about how well working memory will operate in every possible task, we can measure storage-specific capacity by preventing or controlling processing strategies.

That is how one can observe a capacity limit of 3 to 5 separate items ( Cowan, 2001 ). In many such studies with rehearsal and grouping curtailed, information was presented (1) in a brief, simultaneous spatial array; (2) in an unattended auditory channel, with attention to the sensory memory taking place only after the sounds ended; (3) during the overt, repetitive pronunciation of a single word by the participant; or (4) in a series with an unpredictable ending, as in running span. These are boundary conditions within which one apparently can observe a handful of concepts in the conscious mind.

These boundary conditions are also of practical use to predict performance when the material is too brief, long, or complex to allow processing strategies such as rehearsal or grouping. For example, in comprehension of an essay, one might have to hold in mind concurrently the major premise, the point made in the previous paragraph, and a fact and an opinion presented in the current paragraph. Only when all of these elements have been integrated into a single chunk can the reader successfully continue to read and understand. Forgetting one of these ideas may lead to a more shallow understanding of the text, or to the need to go back and re-read. As Cowan (2001) noted, many theorists with mathematical models of particular aspects of problem-solving and thought have allowed the number of items in working memory to vary as a free parameter, and the models seem to settle on a value of about 4, where the best fit is typically achieved.

In recent articles, we have shown the constancy of working memory capacity in chunks, by teaching new multi-item chunks. We have presented a set of arbitrarily-paired words, such as desk-ball , repeatedly with consistent pairing. Concurrently, we have presented other words as singletons. The paired words become new chunks. Young adults can recall 3 to 5 chunks from a presented list no matter whether these are learned pairs or singletons. The most precise result was obtained by Chen and Cowan (in press) as illustrated in Figure 1 . Ordinarily, the result would depend on the length of the list and of the items but, when verbal rehearsal was prevented by having the participant repeat the word “the” throughout the trial, individuals remembered only about 3 units, no matter whether those were singletons or learned pairs. With similar results across many types of materials and tasks, we believe there truly is a central working memory faculty limited to 3–5 chunks in adults, which can predict mistakes in thinking and reasoning ( Halford, Cowan, & Andrews, 2007 ).

Illustration of the three-part method of Chen and Cowan (in press) using word lists, and the key result. The central capacity limit, which can be observed only if rehearsal is prevented, was about 3 chunks no matter whether these chunks were singletons or learned word pairs.

One can ask how individuals differ in working memory ability. They may differ in how much can be stored. There are also processes, though, that can influence how effectively working memory is used. An important example is in the use of attention to fill working memory with the items one should be remembering (say, the concepts being explained in a class) as opposed to filling it with distractions (say, what one is planning to do after class). According to one type of view (e.g., Kane, Bleckley, Conway, & Engle, 2001 ; Vogel, McCollough, & Machizawa, 2005 ), low-span individuals remember less because they use up more of their storage capacity holding information that is irrelevant to the assigned task.

Several other recent studies show, however, that this popular view cannot be the whole story and that there are true capacity differences between individuals ( Cowan, Morey, AuBuchon, Zwilling, and Gilchrist, in press ; Gold et al., 2006 ). Cowan et al. compared 7–8-year-old and 11–12-year-old children and college students, using a version of the array memory procedure illustrated in Figure 2 . There were two different shapes but participants were sometimes instructed to retain only items of one shape. To make the task interesting to children, the colored shapes were to be thought of as children in a classroom. When the test probe item was presented, the task was to indicate with a mouse click whether that “child” was in the correct seat, belonged in a different seat, or belonged out (was missing entirely from the memory array). In the latter case, a click on the door icon sent the “child” to the principal.

Illustration of the method of Cowan et al. (in press) using object arrays, and the key result. For simple materials, the capacity limit increased markedly from age 7 to adulthood, whereas the ability to focus on the relevant items and to ignore irrelevant ones stayed rather constant across that time.

We estimated the contents of working memory in several attention conditions. In one condition, objects of one shape were to be attended, and the test probe item was of that shape on 80% of the trials. In the remaining 20% of the trials in that condition, an item of the shape to be ignored was nevertheless tested. The test probe sometimes differed in color from the corresponding array item. We scored the proportion of change trials in which the change was noticed (hits), and of no-change trials in which an incorrect response of change was given (false alarms). Hits and false alarms contributed to a simple formula indicating the number of items stored in working memory ( Cowan, 2001 ). This value was lower for 7-year-olds (~1.5) than for older children or adults (~3.0), indicating that the age groups differed in storage. There was also an advantage for the test of the shape to be remembered, compared to the shape to be ignored; attention helped greatly. What was noteworthy is that this advantage for the attended shape was just as large in 7-year-olds as in adults, provided that the total number of items in the field was small (4). This suggests that simple storage capacity, and not just processing ability, distinguishes young children from adults. Other work suggests that storage and processing capacities both make important, partly separate and partly overlapping contributions to intelligence and development ( Cowan, Fristoe, Elliott, Brunner, & Saults, 2006 ).

The inclusive versus central distinction has to do with whether we allow individuals to use transient information that is specific to how something sounds, looks, or feels, that is, sensory-modality-specific information; or whether we structure our stimulus materials to exclude that type of information, leaving a residual of only abstract information that applies across modalities (called central information). Although it is important that people can use vivid memories of how a picture looked or how a sentence sounded, these types of information tend to obscure the finding of a central memory usually limited to 3–5 items in adults. That central memory is especially important because it underlies problem-solving and abstract thought.

Central limits can be observed better if the contribution of information in sensory memory is curtailed, as shown by Saults & Cowan (2007) in a procedure illustrated in Figure 3 . An array of colored squares was presented at the same time as an array of simultaneous spoken digits produced by different voices in four loudspeakers (to discourage rehearsal). The task was sometimes to attend to only the squares or only the spoken digits, and sometimes to attend to both modalities at once. The key finding was that, when attention was directed different ways, a central working memory capacity limit still held. People could remember about 4 squares if asked to attend only to squares and, if they were asked to attend to both squares and digits, they could remember fewer squares, but about 4 items in all. This fixed capacity limit was obtained, though, only if the items to be recalled were followed by a jumble of meaningless, mixed visual and acoustic stimuli (a mask) so that sensory memory would be wiped out and the measure of working memory would be limited to central memory. With an inclusive situation (no mask), two modalities were better than one. Cowan and Morey (2007) similarly found that, for the process of encoding (putting into working memory) some items while remembering others, again two modalities are better than one ( Cowan & Morey, 2007 ), whereas modality did not matter for central storage in working memory after encoding was finished.

Illustration of the method in the fifth and final experiment in Saults and Cowan (2007) using audiovisual arrays, and key results. When sensory memory was eliminated, capacity was about 4 items no matter whether these were all visual objects or were a mixture of visual and auditory items.

WHY THE STORAGE CAPACITY LIMIT?

The reasons for the central working memory storage limit of 3–5 chunks remain unclear but Cowan (2005) reviewed a variety of hypotheses. They are not necessarily incompatible; more than one could have merit. There are two camps: (1) capacity limits as weaknesses, and (2) capacity limits as strengths.

The capacity-limit-as-weakness camp suggests reasons why it would be biologically expensive for the brain to have a larger working memory capacity. One way this could work is if there is a cycle of processing in which the patterns of neural firing representing, say, four items or concepts must fire in turn within, say, every consecutive 100-millisecond period, or else not all concepts will stay active in working memory. The representation of a larger number of items could fail because together they take too long to be activated in turn, or because patterns too close together in time produce interference between the patterns (with, for example, a red square and a blue circle being mis-remembered as a red circle and a blue square).

If the neural patterns for multiple concepts are instead active concurrently, it may be that more than about four concepts result in interference among them, or that separate brain mechanisms are assigned to each concept, with insufficient neurons at some critical locale to keep more than about four items active at once. The suggested readings discuss neuroimaging studies showing that one brain area, the inferior parietal sulcus, appears capacity-limited at least for visual stimuli. If capacity is a weakness, perhaps superior beings from another planet can accomplish feats that we cannot because they have a larger working memory limit, similar to our digital computers (which, however, cannot do complex processing to rival humans in key ways).

The capacity-limit-as-strength camp includes diverse hypotheses. Mathematical simulations suggest that, under certain simple assumptions, searches through information are most efficient when the groups to be searched include about 3.5 items on average. A list of three items is well-structured with a beginning, middle, and end serving as distinct item-marking characteristics; a list of five items is not far worse, with two added in-between positions. More items than that might lose distinctiveness within the list. A relatively small central working memory may allow all concurrently-active concepts to become associated with one another (chunked) without causing confusion or distraction. Imperfect rules, such as those of grammar, can be learned without too much worry about exceptions to the rule, as these are often lost from our limited working memory. This could be an advantage, especially in children.

Tests of working memory demonstrate practical limits that vary, depending on whether the test circumstances allow processes such as grouping or rehearsal, focusing of attention on just the material relevant to the task, and the use of modality- or material-specific stores to supplement a central store. Recent work suggests, nevertheless, that there is an underlying limit on a central component of working memory, typically 3–5 chunks in young adults. If we are careful about stimulus control, central capacity limits are useful in predicting which thought processes individuals can execute, and in understanding individual differences in cognitive maturity and intellectual aptitude. There are probably factors of biological economy limiting central capacity but, in some ways, the existing limits may be ideal, or nearly so, for humans.

RECOMMENDED READINGS

Baddeley, A. (2007). Working memory, thought, and action . New York: Oxford University Press. This book provides a thoughtful update of the traditional working memory theory taken in its broad context, including discussion of the recent episodic buffer component that may share characteristics with the central storage capacity concept.

Cowan, N., & Rouder, J.N. (in press) . Comment on “Dynamic shifts of limited working memory resources in human vision.” Science . This article provides a mathematical foundation for the concept of a fixed capacity limit and defends it against the alternative hypothesis that attention can be spread thinly over all item presented to an individual.

Cowan, N. (2005) . See reference list. This book elaborates on the article by Cowan (2001) that is a cornerstone of the capacity limit research, presenting the case for a central storage limit in the context of the history of the field, drawing key distinctions, and exploring alternative theoretical explanations for the limit.

Jonides, J., Lewis, R.L., Nee, D.E., Lustig, C.A., Berman, M.G., & Moore, K.S. (2008). The mind and brain of short-term memory. Annual Review of Psychology, 59 , 193–224. This review article broadly overviews the working memory system, taking into consideration both behavioral and brain evidence and discussing capacity limits along with other possible limitations, such as decay.

Klingberg, T. (2009). The overflowing brain: Information overload and the limits of working memory . New York: Oxford University Press. This book broadly and simply discusses recent research on the concept of working memory capacity, with emphasis on brain research, working memory training, and practical implications of capacity limits.

Acknowledgments

This research was supported by NIH Grant R01 HD-21338. To readers in the 26 th century or thereafter: The title alludes to The magical mystery tour , one of many electromechanically recorded collections of rhythmic, voice-and-instrumental music about life and emotions by the Beatles, a British foursome that had messianic popularity.

• Baddeley AD, Hitch G. Working memory. In: Bower GH, editor. The psychology of learning and motivation. Vol. 8. New York: Academic Press; 1974. pp. 47–89. [ Google Scholar ]
• Chen Z, Cowan N. Core verbal working memory capacity: The limit in words retained without covert articulation. Quarterly Journal of Experimental Psychology (in press) [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Cowan N. The magical number 4 in short-term memory: A reconsideration of mental storage capacity. Behavioral and Brain Sciences. 2001; 24 :87–185. [ PubMed ] [ Google Scholar ]
• Cowan N. Working memory capacity. Hove, East Sussex, UK: Psychology Press; 2005. [ Google Scholar ]
• Cowan N, Fristoe NM, Elliott EM, Brunner RP, Saults JS. Scope of attention, control of attention, and intelligence in children and adults. Memory & Cognition. 2006; 34 :1754–1768. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Cowan N, Morey CC. How can dual-task working memory retention limits be investigated? Psychological Science. 2007; 18 :686–688. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Cowan N, Morey CC, AuBuchon AM, Zwilling CE, Gilchrist AL. Seven-year-olds allocate attention like adults unless working memory is overloaded. Developmental Science (in press) [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Gilchrist AL, Cowan N, Naveh-Benjamin M. Working memory capacity for spoken sentences decreases with adult aging: Recall of fewer, but not smaller chunks in older adults. Memory. 2008; 16 :773–787. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Gold JM, Fuller RL, Robinson BM, McMahon RP, Braun EL, Luck SJ. Intact attentional control of working memory encoding in schizophrenia. Journal of Abnormal Psychology. 2006; 115 :658–673. [ PubMed ] [ Google Scholar ]
• Halford GS, Cowan N, Andrews G. Separating cognitive capacity from knowledge: A new hypothesis. Trends in Cognitive Sciences. 2007; 11 :236–242. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Kane MJ, Bleckley MK, Conway ARA, Engle RW. A controlled-attention view of working-memory capacity. Journal of Experimental Psychology: General. 2001; 130 :169–183. [ PubMed ] [ Google Scholar ]
• Miller GA. The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychological Review. 1956; 63 :81–97. [ PubMed ] [ Google Scholar ]
• Miller GA, Galanter E, Pribram KH. Plans and the structure of behavior. New York: Holt, Rinehart and Winston, Inc; 1960. [ Google Scholar ]
• Saults JS, Cowan N. A central capacity limit to the simultaneous storage of visual and auditory arrays in working memory. Journal of Experimental Psychology: General. 2007; 136 :663–684. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Vogel EK, McCollough AW, Machizawa MG. Neural measures reveal individual differences in controlling access to working memory. Nature. 2005; 438 :500–503. [ PubMed ] [ Google Scholar ]

Psychology Undergraduates Participate in Pathways to Graduate School: Summer Research Program

We are excited to share the work of psychology undergraduates in the summer program  Pathways to Graduate School :  Seyram Agudu ,  Aleeza Amin ,  Kalina Berg , and  Ev Cho .

Pathways is an intensive 10-week summer program where undergraduate students work full-time with a faculty mentor on a research project. This includes a series of seminars preparing students for graduate school and developing research skills.

Seyram Agudu, “ Threat Memory Consolidation Therapy: A Three-Session Manual to Treat Intrusive Memories in PTSD ”

Aleeza Amin, “The Role of Intersectional Identities in Medical Student Experiences”

Kalina Berg, "Courageous Conversations? Exploring White Mothers’ Conversations on Race to Cultivate Antiracist Parenting”

Ev Cho, “ Exploratory Linguistic Analysis of Interviews with Adolescents with Depression After a Creative Arts Intervention ”

Composed by Madison Stromberg, communications assistant.

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