working memory model essay

Working Memory Model (Baddeley and Hitch)

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Saul McLeod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

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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).

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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.

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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.

Reviewed by:

Copyright © 2018 Chai, Abd Hamid and Abdullah. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Working Memory (Definition + Examples)

It’s easy to separate our brain into two sections: short-term memory storage and long-term memory storage. But research has shown that this model is too simplistic.

Where does daydreaming fit in? How do we apply skills and knowledge that are stored in our long-term memory to calculate problems that exist in our short-term memory? How do we explain that time when you thought you were calling someone by their name, but accidentally referred to them as someone else? 

We will review all of the answers to these questions in this video about working memory . Working memory explains more than just the connections between short-term and long-term memory storage. It gives us an insight to how we create, perceive, and tell stories about the world around us. 

What Is Working Memory? 

Working Memory is the function of short term memory that processes language and perception data in the brain. This memory allows us to manipulate objects, items, and numbers to perform complex tasks. Intelligence and working memory are very closely related.

Peter Doolittle describes working memory as “that part of our consciousness that we are aware of at any given time of day.” He gave a TED Talk in 2013 all about how working memory helps us make sense of the world. 

He describes the four parts of working memory:

• Temporarily storing immediate experience into short-term memory storage
• Reaching back into long-term memory 
• Mixing and processing the experience and memories together 
• Applying the meaning discovered from this process to the task at hand 

Working memory is one of the three main executive functions that help people organize tasks, regulate emotions, and pay attention in the moment. If you are a fan of meditation or mindfulness, working memory is crucial to these activities or “state of mind.”

In the TED Talk, Doolittle asked audience members to memorize a set of five words. He then gives the audience a multiplication problem and other tasks to complete. If the audience could remember the five words after those simple tasks, they could congratulate themselves with a high working memory capacity. (We will share some more examples on how to assess your working memory later in this video.) 

How Working Memory Applies to Intelligence 

If you’ve got a good working memory, you should be quite pleased with yourself. According to Peter Doolittle, people with good working memory tend to be good storytellers and score higher on standardized tests.

A good working memory allows someone to remember information while recalling other pieces of information or performing other functions. And while more research still has to be done, many experts say that working memory is a good predictor of general intelligence. 

Central Executive Memory and How Working Memory is Organized

How does our working memory process information? Researchers are still trying to answer this question, but they have created a diagram that shows the organization and flow of information through our working memory. 

The most well-known model showing this process is the Working Memory Model, created by Baddeley and Hitch in 1974.

Once we decide to draw attention to sensory input, it goes into our Central Executive Memory. This is the “manager” of the operations that working memory completes. The Central Executive Memory system delegates tasks.

What input is most important? What parts of the working memory system will handle the information? And what ends up continuing the process into long-term memory? 

Psychologists know the basics of what Central Executive Memory does, but the process in which it is done isn’t so clear. Much more is known about the areas of the brain where the CEM delegates the processing of information. 

These areas include the Phonological Loop, Episodic Buffer, and VisuoSpatial Sketchpad. 

Phonological Loop

The Phonological Loop handles all of the auditory information. Within this loop are the areas of the brain that process what we hear and rehearse what we are going to say. When people are asked to memorize a phone number or a set of words, the Phonological Loop is put in charge. 

It's called a loop because if the loop is too long, you can't start the process over. For example, try to remember the numbers "5-6-2-7-3". Say them in your head over and over again. Now close your eyes and say those 5 numbers again. You probably did it, right?

Now, try to member these numbers "5-6-2-7-3-2-8-1-5-8-9-2-4". You can't remember it, can you? That's because it's too long to fit inside the phonological 'loop'. By the time you get to the first 8, you have already forgotten the first number. 

The Phonological loop can also hold visual information that is turned into semantic information in working memory. For example, if you see a sign that says "slow down, turtles ahead". You can turn the visual information on the sign into auditory information by saying the phrase in your head. 

VisuoSpatial Sketchpad

So now we know what’s in charge of what we hear. But what about what we see? This is reserved for the VisuoSpatial Sketchpad. The images that we take in and create in our minds are all handled by this area of the brain. 

Think of a map from your house to your best friends house. You probably are seeing a top-down map with a line across each of the roads to get there. This place is called the VisuoSpatial Sketchpad. 

Colors, Shapes, and even Haptic feedback are all information that is stored in our 'mind's doodlebook'. 

Episodic Buffer

The Episodic Buffer is the area that adds the soundtrack to the visuals. Like a movie, it puts together visual and auditory information and adds a sense of timing and organization. When our minds start to wander and daydreams start to form, the episodic buffer is hard at work “dubbing” the lines to the scene.

The Episodic Buffer also adds smell and taste information. Baddeley says this 4th and last component of the model helps buffer information between working memory and long term memory. 

What's the reason for adding it? In highly intelligent amnesiacs, patients show no ability to encode  new information in long term memory. However, they do have good short-term recall of stories and events, which require mores space than just the phonological loop. Here's Baddeley's own opinion: 

The episodic buffer appears...capable of storing bound features and making them available to conscious awareness but not itself responsible for the process of binding

And yes, when you daydream, your working memory is working. In fact, studies show that daydreaming can be a sign that you have a larger working memory capacity. 

Remember, working memory does have a capacity. It can only take in so much information. There is a lot that your senses take in that doesn’t go into your working memory. 

Decay Theory

Information only reaches your working memory if it is given attention. If you make an effort to actively maintain the information, through repetition, evaluation, or other means, it will make its way into your working memory and maybe into your long-term memory. 

Without attention, the information begins to decay. This is the idea behind the Decay Theory. The decay theory says that the sensory input we consume leaves a physical and chemical change, referred to as a trace, in our minds. Over time, if the information is not addressed, that trace starts to decay until it is dropped from memory entirely.

If you keep having to feed your dog every day, then you're giving attention to the task. However, if your dog dies, and you no longer have to feed your dog, then the attention is lacking, and over time your brain will assume "there's no need to remember this". Many people with dogs that have passed do not remember specific times of actually feeding their dogs. 

The decay theory attempts to answer questions about how and why certain pieces of information are forgotten. But it’s almost an impossible theory to prove. When researchers give participants information as part of a study, the participants are very likely to pay attention to that information, therefore moving the information along to their working memory before it has a chance to decay or not decay.  

Effects of Stress

Why does interference occur? Our current situation will always add input to our long-term memories. This is an important lesson to learn when it comes to working memory and how we recall past events. The present moment always shapes our perception of what happened in the past. 

I say this now because there are many things that can impact our working memory’s capacity and ability to accurately mix and process sensory input with long-term memories. One of these things is stress. Multiple studies continue to show that stress is associated with a working memory deficit. Stress greatly impacts working memory, and not always in a positive way. 

Fast Reactions 

Let’s start with the one positive note on stress and working memory. Stress, in the primal sense, is a signal that a person is in danger. The release of cortisol (the stress hormone) puts us into “survival mode.”

Studies have known that due to high stress levels, working memory works  faster. Humans need a faster reaction time in moments when they have to choose between fight or flight. So a little bit of stress can help you process information faster.

Mistakes 

Unfortunately, the information that you process is not always correct.

​ Stress taints the information that we both take in around us and the memories that we pull from our long-term memory storage. Have you ever heard stories of witnesses in a criminal case who can’t seem to give a consistent answer on what they saw?

They may even change their story throughout questioning. This is partially due to the effects of stress. Someone under high levels of stress may not be able to pull up information or specific details from their long-term memory. 

The best way to prevent these mistakes is to stay calm under pressure. Stay present and take a long, deep breath. These breaths tell the brain that you are in a safe situation and that there is no need to release anymore stress hormones that work against working memory. 

Effects of Alcohol

Have you ever woken up from a night of partying and asked yourself, “What happened last night?” We all know that too much alcohol can significantly affect short-term memory. But how does alcohol affect your working memory? 

Alcohol and working memory have an interesting relationship. The studies done thus far on alcohol and working memory show that booze only affects some processes and strategies implemented by working memory. 

​A glass of wine at dinnertime is not considered a threat to your working memory. But people with chronic alcoholism are likely to experience a loss of ability to stay focused and function using the VisuoSpatial Sketchpad . 

Interestingly enough, one study also concluded that working memory and alcohol consumption negatively affect each other in a circle. A loss of working memory capacity results in a loss of inhibitions, making it more likely for people to grab another drink. The more drinks someone has in a day, the harder it will be for working memory to complete functions. So on and so forth.

There is a lot more work to be done to figure out how alcohol actually interacts with working memory and causes negative effects. But here’s my advice: don’t get wasted if you want to be able to solve tasks or learn something new. 

Tasks to Assess and Measure Working Memory 

How is your working memory? You can use a variety of different tests to help you determine how your working memory compares to others. 

I have actually designed the first every 3-in-1 memory test to measure short term, working, and long term memory. You can take it for free on my website in less than 5 minutes. 

Sternberg Memory Task 

The first is the Sternberg Memory Task. You can use this assessment online and figure out how fast your working memory works. The assessment flashes a set of letters on the screen for a few seconds. Then, it asks you to identify whether a single letter was part of the set. Your reaction time, and whether or not you were correct, are both recorded.  

N-back task

The N-back task is a significantly harder version of the Sternberg Memory Task. You can use this tool online . Rather than asking participants to determine whether a particular letter just appeared on the screen, participants are asked to recall whether the letter was the same letter that appeared three trials prior. That’s a lot of letters and orders to keep in your head! 

Related posts:

• Beck’s Depression Inventory (BDI Test)
• The Psychology of Long Distance Relationships
• Operant Conditioning (Examples + Research)
• Variable Interval Reinforcement Schedule (Examples)
• Concrete Operational Stage (3rd Cognitive Development)

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Memory Tricks

Memory Palace

Rote Memorization

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Working Memory Underpins Cognitive Development, Learning, and Education

Working memory is the retention of a small amount of information in a readily accessible form. It facilitates planning, comprehension, reasoning, and problem-solving. I examine the historical roots and conceptual development of the concept and the theoretical and practical implications of current debates about working memory mechanisms. Then I explore the nature of cognitive developmental improvements in working memory, the role of working memory in learning, and some potential implications of working memory and its development for the education of children and adults. The use of working memory is quite ubiquitous in human thought, but the best way to improve education using what we know about working memory is still controversial. I hope to provide some directions for research and educational practice.

What is Working Memory? An Introduction and Review

Working memory is the small amount of information that can be held in mind and used in the execution of cognitive tasks, in contrast with long-term memory, the vast amount of information saved in one’s life. Working memory is one of the most widely-used terms in psychology. It has often been connected or related to intelligence, information processing, executive function, comprehension, problem-solving, and learning, in people ranging from infancy to old age and in all sorts of animals. This concept is so omnipresent in the field that it requires careful examination both historically and in terms of definition, to establish its key characteristics and boundaries. By weaving together history, a little philosophy, and empirical work in psychology, in this opening section I hope to paint a clear picture of the concept of working memory. In subsequent sections, implications of working memory for cognitive development, learning, and education will be discussed in turn, though for these broad areas it is only feasible to touch on certain examples.

Some researchers emphasize the possibility of training working memory to improve learning and education. In this chapter, I take the complementary view that we must learn how to adjust the materials to facilitate learning and education with the working memory abilities that the learner has. Organizing knowledge, for example, reduces one’s memory load because the parts don’t have to be held in mind independently.

Take, for example, the possibility of doing some scouting ahead so that you will know what this article is about, making your task of reading easier. If you tried to read through the headings of this article, you might have trouble remembering them (placing them all in working memory) so as to anticipate how they fit together. If you read Figure 1 , though, it is an attempt to help you organize the information. If it helps you associate the ideas to one another to build a coherent framework, it should help you read by reducing the working-memory load you experience while reading. In doing so, you are building a rich structure to associate the headings with one another in long-term memory (e.g., Ericsson & Kintsch, 1995 ), which reduces the number of ideas that would have to be held independently in working memory in order to remember the organization.

Schematic diagram of the arguments in the present article.

Early History of Working Memory Research

In 1690, John Locke distinguished between contemplation, or holding an idea in mind, and memory, or the power to revive an idea after it has disappeared from the mind ( Logie, 1996 ). The holding in mind is limited to a few concepts at once and reflects what is now called working memory, as opposed to the possibly unlimited store of knowledge from a lifetime that is now called long-term memory. Working memory can be defined as the small amount of information that can be held in an especially accessible state and used in cognitive tasks.

Philosophers have long been interested in the limits of what can be contemplated, as noted by a leading British economist and logician, William Stanley Jevons. In an article in Science in 1871, he mused (p. 281): “It is well known that the mind is unable through the eye to estimate any large number of objects without counting them successively. A small number, for instance three or four, it can certainly comprehend and count by an instantaneous and apparently single act of mental attention.” Then he devised a little experiment to test this hypothesis, on himself. On each trial, he casually reached into a jar full of beans, threw several beans onto a table, and tried to estimate their number without counting. After 1,027 trials, he made no errors for sets of 3 or 4 beans, with some small errors for sets of 5 beans, and with increasing magnitudes of error as a function of set size thereafter, up to 15 beans. Despite the problematic nature of the method (in that the bean thrower was also the bean judge), the finding that normal adults typically can keep in mind only about 3 or 4 items has been replicated many times in modern research, using methods similar to Jevons (e.g., Mandler & Shebo, 1982 ) and using many other methods ( Cowan, 2001 ). The limited amount that could be held in mind at once played an important role in early experimental psychology, e.g., in the early experimental work of Hermann Ebbinghaus (1885/1913) and Wilhelm Wundt (1894/1998) . On the American front, William James (1890) wrote about a distinction between primary memory, the items in consciousness and the trailing edge of what is perceived in the world, and secondary memory, the items in storage but not currently in consciousness. Recent investigators have considered multiple possible reasons why primary memory might be limited to just a few items at once, including biological accounts based on the need to avoid confusion between concurrent objects in memory, and evolutionary and teleological accounts based on ideas about what capacity might be ideal for learning and memory retrieval ( Cowan, 2010 ; Sweller, 2011 ), but as yet the reason is unknown.

Ubiquity of the Working Memory Concept

When we say that working memory holds a small amount of information , by this term we may be referring to something as abstract as ideas that can be contemplated, or something as concrete as objects that can be counted (e.g., beans). The main point of information is that it is the choice of some things out of a greater set of possible things. One of the exciting aspects of working memory is that it may be important on so many different levels, and in so many different situations. When you are listening to language, you need to retain information about the beginning of the sentence until you can make sense of it. If you hear Jean would like to visit the third building on the left you need to recall that the actor in the sentence is Jean. Then you need to retain the verb until you know what it is she would like to visit, and you need to retain the adjective “third” until you know, third what; and all of the pieces must be put together in the right way. Without sufficient working memory, the information would be lost before you could combine it into a coherent, complete thought. As another example of how working memory is used, when doing simple arithmetic in your head, if you want to add 24 and 18 you may need to find that 4+8=12, retain the 2 while carrying the 1 over to the tens column to make 2+1+1=4 in the tens column, and integrate with the ones columns to arrive at the answer, 42. As a third example, if you are searching for your car in a parking lot, you have to remember the layout of the cars in the region you just searched so that you can avoid wasting time searching the same region again. In the jungle, a predator that turns its vision away from a scene and revisits it moments later may use working memory to detect that something in the scene has shifted; this change detection may indicate the presence of prey.

So the information in working memory can range from spoken words and printed digits to cars and future meals. It can even encompass abstract ideas. Consider whether a young child can get a good understanding of what is or is not a tiger (a matter of word category concepts, e.g., Nelson, 1974 ; Saltz, Soller, & Sigel, 1972 ). The concept is, in lay terms, a big cat with stripes. It excludes lions, which have no stripes, and it excludes zebras, which are not big cats. The child must be able to keep in mind the notion of a cat and the notion of stripes at the same time in order to grasp the tiger concept correctly. If the child thinks only of the stripes, he or she may incorrectly label a zebra as a tiger. The concept presumably starts out in working memory and, once it is learned, is transferred to long-term memory. At first, an incomplete concept might be stored in long-term memory, leading to misconceptions that are corrected later when discrepancies with further input are noticed and working memory is used to amend the concept in long-term memory. On a more abstract plane, there are more semantic issues mastered somewhat later in childhood (e.g., Clark & Garnica, 1974 ). The concept of bringing something seems to require several conditions: the person doing the bringing must have something at a location other than the speaker’s location (or future planned location), and must accompany that thing to the speaker’s location. You can ask the person to bring a salad to your house, but probably not to take a salad to your house (unless you are not there), and not to send a salad to your house (unless they are not coming along). These conditions can tax working memory. Again, the child’s initial concept transferred from working memory to long-term memory may be incomplete, and amended later when discrepancies with further input are noticed.

Working Memory: The Past 64 Years

There are several modern beginnings for the working memory concept. Hebb (1949) had an outlook on temporary memory that was more neurologically based than the earlier concept of primary memory of James (1890) . He spoke of ideas as mediated by assemblies of cells firing in a specific pattern for each idea or concept, and only a few cell assemblies would be active, with current neural firing, at any moment. This vision has played an important role in the field. An issue that is raised by this work is whether working memory should be identified with all of the active information that can be used in immediate memory tests, whether conscious or not, or whether it should be reserved to describe only the conscious information, more in the flavor of James. Given that working memory is a term usually used to explain behavioral outcomes rather than subjective reports, it is typically not restricted to conscious primary memory (e.g., see Baddeley, 1986 ; Baddeley & Hitch, 1974 ; Cowan, 1988 ). Cowan explicitly suggested that there are two aspects of working memory storage: (1) the activated portion of long-term memory, perhaps corresponding to Hebb’s active cell assemblies, and (2) within that activated portion, a smaller subset of items in the focus of attention. The activated memory would consist of a fragmented soup of all kinds of activated features (sensory, phonological, orthographic, spatial, and semantic), whereas the focus of attention would contain just a few well-integrated items or chunks.

Contributions of George Miller

Miller (1956) discussed the limitation in how many items can be held in immediate memory. In the relevant test procedure, a list of items is seen or heard and immediately afterward (that is, with no imposed retention interval), the list must be repeated verbatim. The ability to do so was said to be limited to about seven chunks, where a chunk is a meaningful unit. For example, the random digit list 582931 may have to be encoded initially as six chunks, one per digit, whereas the sequence 123654 probably can be encoded by most adults as only two chunks (an ascending triplet followed by a descending triplet). Subsequent work has suggested that the number seven is a practical result that emerges on the basis of strategies that participants use and that, when it is not possible to use chunking or covert verbal rehearsal to help performance, adults typically can retain only 3 or 4 pre-existing chunks ( Chen & Cowan, 2009 ; Cowan, 2001 ; Cowan, Rouder, Blume, & Saults, 2012 ; Luck & Vogel, 1997 ; Rouder et al., 2008 ).

The first mention I have found of the term working memory comes from a book by Miller, Galanter, and Pribram (1960) , Plans and the structure of behavior . The title itself, and the concept of organization, seems reminiscent of the earlier work by Hebb (1949) , The organization of behavior . Miller et al. observed that daily functioning in the world requires a hierarchy of plans. For example, your plan to do well at work requires a sub-plan to be there at time in the morning, which in turn may require sub-plans to eat breakfast, shower, get dressed, gather work materials, and so on. Each of these plans also may have sub-plans, and you may have competing plans (such as choosing an after-work activity, calling your mother, or acquiring food for dinner). Our working memory was said to be the mental faculty whereby we remember the plans and sub-plans. We cannot think about all of them at once but we might, for example, keep in mind that the frying pan is hot while retrieving a knife from the drawer, and we may keep bringing to mind the approximate time so as not to be late. Working memory was said to be the facility that is used to carry out one sub-plan while keeping in mind the necessary related sub-plans and the master plan.

Contributions of Donald Broadbent

In Great Britain, Broadbent’s (1958) book helped to bring the conversation out of the behaviorist era and into an era of cognitive psychology. In a footnote within the book, he sketched a rough information processing diagram that showed information progressing from a sensory type of store that holds a lot of information briefly, through an attention filter to essentially a working memory that holds only a few items, to a long-term memory that is our storehouse of knowledge accumulated through a lifetime. The empirical basis for the model came largely from his work with selective attention, including many dichotic listening studies in which the task was to listen to the message from one ear and ignore the message from the other ear, or report both messages in some order. The motivation for this kind of research came largely from practical issues provoked by World War II, such as how to help a pilot listen to his own air traffic control message while ignoring messages meant for other pilots but presented in the same channel. An important theoretical outcome, however, was the discovery of a difference between a large-capacity but short-lived sensory memory that was formed regardless of attention, and a longer-lived but small-capacity abstract working memory that required attention.

Contributions of Alan Baddeley and Graham Hitch

Miller et al. (1960) may have devised the term working memory but they were not the predominant instigator of the work that has occurred subsequently in the field. Google Scholar does show it with over 5,600 citations. A chapter by Baddeley and Hitch (1974) , though, is listed with over 7,400 citations and a 1992 Science article summarizing that approach has over 14,500 citations. In the 1974 chapter, the term working memory was used to indicate a system of temporary memory that is multifaceted, unlike the single store such as James’ primary memory, or the corresponding box in Broadbent’s (1958) model, or an elaborated version of it as in the model of Atkinson and Shiffrin (1968) model, none of which would do. In fact, a lot of investigators in the 1960’s proposed variations of information processing models that included a single short-term memory store, and Baddeley often has referred to these together, humorously, as the “modal model,” providing a sketch of it with sensory memory, short-term memory, and long-term memory boxes as in the Broadbent and the Atkinson/Shiffrin models. (When the humor and the origin of the phrase “modal model” are forgotten, yet the phrase is still widely used, it seems sad somehow.)

The main point emphasized by Baddeley and Hitch (1974) is that there were diverse effects that appeared to implicate short-term memory, but that did not converge to a single component. Phonological processing interfered most with phonological storage, visual-spatial processing interfered with visual-spatial storage, and a working memory load did not seem to interfere much with superior memory for the end of a list, or recency effect. Conceptual learning did not depend heavily on the type of memory that was susceptible to phonological similarity effects, and a patient with a very low memory span was still able to learn new facts. To account for all of the dissociations, they ended up concluding that there was an attention-related control system and various storage systems. These included a phonological system that also included a covert verbal rehearsal process, and a visual-spatial storage system that might have its own type of non-verbal rehearsal. In the 1974 version of the theory, there were attention limits on the storage of information as well as on processing. In a 1986 book, Baddeley eliminated the attention-dependent storage but in a 2000 paper, a new component was added in the form of an episodic buffer. This buffer might or might not be attention-dependent and is responsible for holding semantic information for the short term, as well as the specific binding or association between phonological and visual-spatial information. Baddeley and Hitch called the assembly or system of storage and processing in service of holding information in an accessible form working memory, the memory one uses in carrying out cognitive tasks of various kinds (i.e., cognitive work).

Model of Cowan (1988)

Through the years, there were several other proposals that alter the flavor of the working memory proposal. Cowan (1988) was concerned with how we represent what we know and do not know about information processing. The “modal models” of which Baddeley has spoken began with Broadbent’s (1958) model in which the boxes were shown to be accessed in sequence, comparable to a computer flow chart: first sensory memory, then an attention filter, then short-term memory, and then long-term memory. Atkinson & Shiffrin (1968) preserved the flow chart structure but added recursive entry into the boxes, in the form of the control processes. Baddeley and Hitch (1974) and Baddeley (1986) instead used a processing diagram in which the boxes could be accessed in parallel. One presumably could enter some information into phonological storage while concurrently entering other information into visual-spatial storage, with interacting modules and concurrent executive control.

Cowan (1988 , 1995 , 1999 , 2001 , 2005 ) recoiled a bit from the modules and separate boxes, partly because they might well form an arbitrarily incomplete taxonomy of the systems in the brain. (Where would spatial information about sound go? Where would touch information go? These types of unanswered questions also may have helped motivate the episodic buffer of Baddeley, 2000 .) There could be multiple modules, but because we do not know the taxonomy, they were all thrown into the soup of activated long-term memory. Instead of separate boxes, I attempted to model on a higher level at which distinctions that were incomplete were not explicitly drawn into the model, and mechanisms could be embedded in other mechanisms. Thus, there was said to be a long-term memory, a subset of which was in an activated state (cf. Hebb, 1949 ), and within that, a smaller subset of which was in the focus of attention (cf. James, 1890 ). Dissociations could still occur on the basis of similarity of features; two items with phonological features will interfere with one another, for example, more than one item with phonological features and another item with only visual-spatial features. The model still included central executive processes.

Compared to Baddeley and Hitch (1974) , Cowan (1988) also placed more emphasis on sensory memory. It is true that printed letters, like spoken letters, are encoded with speech-based, phonological features that can be confused with each other in working memory (e.g., Conrad, 1964 ). Nevertheless, there is abundant other evidence that lists presented in a spoken form are remembered much better, in particular at the end of the list, than verbal lists presented in printed form (e.g., Murdock & Walker, 1969 ; Penney, 1989 ).

The attention filter also was internalized in the model of Cowan (1988) . Instead of information having to pass through a filter, it was assumed that all information activates long-term memory to some degree. The mind forms a neural model of what it has processed. This will include sensory information for all stimuli but, in the focus of attention, much more semantic information than one finds for unattended information. Incoming information that matches the current neural model becomes habituated, but changes that are perceived cause dishabituation in the form of attentional orienting responses toward the dishabituated stimuli (cf. Sokolov, 1963 ). Such a system has properties similar to the attenuated filtering model of Treisman (1960) or the pertinence model of Normal (1968) . Attention is controlled in this view dually, often with a struggle between voluntary executive control and involuntary orienting responses.

How consistent is Cowan (1988) with the Baddeley and Hitch model? Contributions of Robert Logie

With the addition of the episodic buffer, the model of Baddeley and Hitch makes predictions that are often similar to those of Cowan (1988) . There still may be important differences, though. An open question is whether the activated portion of long-term memory of Cowan (1988) functionally serves the same purpose as the phonological and visual-spatial buffers of Baddeley and Hitch (1974) and Baddeley (1986) . Robert Logie and colleagues argue that this cannot be, inasmuch as visual imagery and visual short-term memory are dissociated ( Borst, Niven, & Logie, 2012 ; Logie & van der Meulen, 2009 ; van der Meulen, Logie, & Della Sala, 2009 ). Irrelevant visual materials interfere with the formation of visual imagery but not with visual storage, whereas tapping in a spatial pattern interferes with visual storage but not the formation of visual images. According to the model that these sources put forward, visual imagery involves activation of long-term memory representations, whereas visual short-term storage is a separate buffer. Although this is a possibility that warrants further research, I am not yet convinced. There could be other reasons for the dissociation. For example, in the study of van der Meulen et al., the visual imagery task involved detecting qualities of the letters presented (curved line or not, enclosed space or not, etc.) and these qualities could overlap more with the picture interference; whereas the visual memory task involved remembering letters in upper and lower case visually, in the correct serial order, and the serial order property may suffer more interference from tapping in a sequential spatial pattern. Testing of the generality of the effects across tasks with different features is needed.

Other models of cross-domain generality

One difference between the Baddeley (1986) framework and that of Cowan (1988) was that Cowan placed more emphasis on the possibility of interference between domains. There has been a continuing controversy about the extent to which verbal and nonverbal codes in working memory interfere with one another (e.g., Cocchini, Logie, Della Sala, MacPherson, & Baddeley, 2002 ; Cowan & Morey, 2007 ; Fougnie & Marois, 2011 ; Morey & Bieler, 2013 ). The domain-general view has extended to other types of research. Daneman and Carpenter (1980) showed that reading and remembering words are tasks that interfere with one another, with the success of remembering in the presence of reading a strong correlate of reading comprehension ability. Engle and colleagues (e.g., Engle, Tuholski, Laughlin, & Conway, 1999 ; Kane et al., 2004 ) showed that this sort of effect does not just occur with verbal materials, but occurs even with storage and processing in separate domains, such as spatial recall with verbal memory. They attributed individual differences primarily to the processing tasks and the need to hold in mind task instructions and goals while suppressing irrelevant distractions.

Barrouillet and colleagues (e.g., Barrouillet, Portrat, & Camos, 2011 ; Vergauwe, Barrouillet, & Camos, 2010 ) emphasized that the process of using attention to refresh items, no matter whether verbal or nonverbal in nature, takes time and counteracts decay. They provided complex tasks involving concurrent storage and processing, like Daneman and Carpenter and like Engle and colleagues. The key measure is cognitive load, the proportion of time that is taken up by the processing task rather than being free for the participant to use to refresh the representations of items to be remembered. The finding of Barrouillet and colleagues has been that the effect of cognitive load on the length of list that can be recalled, or memory span, is a negative linear (i.e., deleterious) effect. They do also admit that there is a verbal rehearsal process that is separate from attentional refreshing, with the option of using either mode of memory maintenance depending on the task demands ( Camos, Mora, & Oberauer, 2011 ), but there is more emphasis on attentional refreshing than in the case of Baddeley and colleagues, and the approach therefore seems more in keeping with Cowan (1988) with its focus of attention (regarding refreshing see also Cowan, 1992 ).

Ongoing controversies about the nature of working-memory memory limits

There are theoretically two basic ways in which working memory could be more limited than long-term memory. First, It could be limited in terms of how many items can be held at once, a capacity limit that Cowan (1998, 2001 ) tentatively ascribes to the focus of attention. Second, it could be limited in the amount of time for which an item remains in working memory when it is no longer rehearsed or refreshed, a decay limit that Cowan (1988) ascribed to the activated portion of long-term memory, the practical limit being up to about 30 seconds depending on the task.

Both of these limits are currently controversial. Regarding the capacity limit, there is not much argument that, within a particular type of stimulus coding (phonological, visual-spatial, etc.), normal adults are limited to about 3 or 4 meaningful units or chunks. The debate is whether the limit occurs in the focus of attention, or because materials of similar sorts interfere with one another (e.g., Oberauer, Lewandowsky, Farrell, Jarrold, & Greaves, 2012 ). In my recent, still-unpublished work, I suggest that the focus of attention is limited to several chunks of information, but that these chunks can be off-loaded to long-term memory and held there, with the help of some attentional refreshing, while the focus of attention is primarily used to encode additional information.

Regarding the memory loss or decay limit, some studies have shown no loss of information for lists of printed verbal materials across periods in which rehearsal and refreshing have apparently been prevented ( Lewandowsky, Duncan, & Brown, 2004 ; Oberauer & Lewandowsky, 2008 ). Nevertheless, for arrays of unfamiliar characters followed by a mask to eliminate sensory memory, Ricker and Cowan (2010) did find memory loss or decay (cf. Zhang & Luck, 2009 ). In further work, Ricker et al. (in press) suggested that the amount of decay depends on how well the information is consolidated in working memory (cf. Jolicoeur & Dell'Acqua, 1998 ). Given that the time available for refreshing appeared to be inversely related to the cognitive load, the consolidation process that seems critical is not interrupted by a mask but continues after it. This consolidation process could be some sort of strengthening of the episodic memory trace based on attentional refreshing in the spirit of Barrouillet et al. (2011) . If so, the most important effect of this refreshing would not be to reverse the effects of decay temporarily, as Barrouillet et al. proposed, but rather to alter the rate of decay itself. Our plans for future research include investigation of these possibilities.

Long-term working memory

It is clear that people function quite well in complex environments in which detailed knowledge must be used in an expert manner, despite a severe limit in working memory to a few ideas or items at once. What is critical in understanding this paradox of human performance is that each slot in working memory can be filled with a concept of great complexity, provided that the individual has the necessary knowledge in long-term memory. This point was made by Miller (1956) in his concept of combining items to form larger chunks of information, with the limit in working memory found in the number of chunks, not the number of separate items presented for memorization. Ericsson and Kintsch (1995) took this concept further by expanding the definition of working memory to include relevant information in long-term memory.

Although we might quibble about the best definition of working memory, it seems undeniable that long-term memory is often used as Ericsson and Kintsch (1995) suggest. An example is what happens when one is holding a conversation with a visitor that is interrupted by a telephone call. During the call, the personal conversation with one’s visitor is typically out of conscious working memory. After the call, however, with the visitor serving as a vivid cue, it is often possible to retrieve a memory of the conversation as a recent episode and to remember where this conversation left off. That might not be possible some days later. This use of long-term memory to serve a function similar to the traditional working memory, thus expanding the person’s capabilities, was termed long-term working memory by Ericsson and Kintsch. Cowan (1995) alluded to a similar use of long-term memory for this purpose but, not wanting to expand the definition of working memory, called the function virtual short-term memory, meaning a use of long-term memory in a way that short-term memory is usually used. It is much like the use of computer memory that allows the computer to be turned off in hibernation mode and later returned to its former state when the memory is retrieved.

Given the ability of humans to use long-term memory so adeptly, one could ask why we care about the severe working memory capacity limit at all. The answer is that it is critical when there is limited long-term knowledge of the topic. In such circumstances, the capacity of working memory can determine how many items can be held in mind at once in order to use the items together, or to link them to form a new concept in long-term memory. This is the case in many situations that are important for learning and comprehension. One simple example of using items together is following a set of instructions, e.g., to a preschool child, put your drawing in your cubby and then go sit in the circle . Part of that instruction may be forgotten before it is carried out and teachers must be sensitive to that possibility. A simple example of linking items together is in reading a novel, when one listens to a description of a character and melds the parts of the description to arrive at an overall personality sketch that can be formed in long-term memory. Inadequate use of working memory during reading may lead to the sketch being incomplete, as some descriptive traits are inadvertently ignored. Knowledge of this working memory limit can be used to improve one’s writing by making it easier to remember and comprehend.

Paas and Sweller (2012) bring up the distinction between biologically primary and secondary knowledge ( Geary, 2008 ) and suggest (p. 29) that “Humans are easily able to acquire huge amounts of biologically primary knowledge outside of educational contexts and without a discernible working memory load.” Examples they offered were the learning of faces and learning to speak. It may well be the case that individual faces or spoken words quickly become integrated chunks in long-term memory (and, I would add, the same seems true for objects in domains of learned expertise, e.g., written words in adults). Nevertheless, the biologically-primary components are used in many situations in which severe capacity limits do apply. In these situations, the added memory demand is considered biologically secondary. An example is learning which face should be associated with which name. If four novel faces are shown on a screen and their names are vocally presented, these name-face pairs cannot be held in working memory at once, so it is difficult to retain the information and it often takes additional study of one pair at a time to remember the name-face pairing.

Specific mathematical models

Here I have been selective in examining models of working memory that are rather overarching and verbally specified. By limiting the domain of applicability and adding some processing assumptions, other researchers throughout the years have been able to formulate models that make mathematical predictions of performance in specific situations. We have learned a lot from them but they are essentially outside of the scope of this review given limited space and given my own limitations. For examples of such models see Brown, Neath, & Chater, 2007 ; Burgess & Hitch, 1999 ; Cowan et al., 2012 ; Farrell & Lewandowsky, 2002 ; Hensen, 1998 ; Murdock, 1982; Oberauer & Lewandowsky, 2011 ). The importance of these models is that they make clear the consequences of our theoretical assumptions. In order to make quantitative predictions, each mathematical assumption must be made explicit. It is sometimes found that the effects of certain proposed mechanisms, taken together, are not what one might assume from a purely verbal theory. Of course, some of the assumptions that one must make to eke out quantitative predictions may be unsupported, so I believe that the best way forward in the field is to use general verbal, propositional thinking some of the time and specific quantitative modeling other times, working toward a convergence of these methods toward a common theory.

Summary: Status of Working Memory

The progress in this field might be likened to an upward spiral. We make steady progress but meanwhile, we go in circles. The issues of the nature of working memory limits have not changed much from the early days. Why is the number of items limited? Why is the duration limited? What makes us forget? How is it related to the conscious mind and to neural processes? These questions are still not answered. At the same time, we have agreement about what can be found in particular circumstances. Set up the stimuli one way, and there is interference between modalities. Set it up another way and there appears to be much less interference. Set it up one way and items are lost rapidly across time. Set it up a different way, and there is much less loss. There are brain areas associated with the focus of attention and with working memory across modalities ( Cowan, 2011 ; Cowan, Li et al., 2011 ; Todd & Marois, 2004 ; Xu & Chun, 2006 ). This is progress awaiting an adequate unifying theory.

What we do know has practical implications. To avoid overtaxing an individual’s working-memory capabilities, one should avoid presenting more than a few items or ideas at once, unless the items can be rapidly integrated. One should also avoid making people hold on to unintegrated information for a very long time. For example, I could write a taxing sentence like, It is said that, if your work is not overwhelming, your car is in good repair, and the leaves have changed color, it is a good time for a fall vacation . However, that sentence requires a lot from the reader’s working memory. I could reduce the working memory load by not making you wait for the information that provides the unifying theme, keeping the working memory load low: It is said that a good time for a fall vacation is when your work is not overwhelming, your car is in good repair, and the leaves have changed color .

Working Memory and Cognitive Development

There is no question that working-memory capabilities increase across the life span of the individual. In early tests of maturation (e.g., Bolton, 1892 ), and to this day in tests of intelligence, children have been asked to repeat lists of random digits. The length of list that can be successfully repeated on some predefined proportion of trials is the digit span. It increases steadily with childhood maturation, until late childhood. When the complexity of the task is increased, the time to adult-like performance is extended a bit further, with steady improvement throughout childhood (for an example see Gathercole, Pickering, Ambridge, & Wearing, 2004 ).

As we saw in the introductory section, clear practical findings do not typically come with a clear understanding of the theoretical explanation. There have been many explanations over the years for the finding of increasing memory span with age (e.g., see Bauer & Fivush, in press ; Courage & Cowan, 2009 ; Kail, 1990 ). These explanations may lead to differing opinions of the best course for learning and education, as well.

Explanations Based on Capacity

Explanations of intellectual growth based on working memory capacity stem from what has been called the neoPiagetian school of thought. Jean Piaget outlined a series of developmental stages, but with no known underlying reason for the progression between stages. Pascual-Leone and Smith (1969) attributed the developmental increases to increases in the number of items that could be held in mind at once.

The theory becomes more explicit with the contributions of Halford, Phillips, and Wilson (1998) and Andrews and Halford (2002) . They suggests that it is the number of associations between elements that is restricted and that this matters because it limits the complexity of thought. In my example above, the concept of a tiger versus lion versus zebra requires concurrent consideration of the animal’s shape and presence or absence of stripes. Similarly, addition requires the association between three elements: the two elements being added and the sum. A concept like bigger than is a logical relation requiring three slots, e.g., bigger than (dog, elephant) . Ratios require the coordination of four elements (e.g., 4/6 is equivalent to 6/9) and therefore are considerably harder to grasp, according to the theory (see Halford, Cowan, & Andrews, 2007 ).

This concept is quite promising and might even appear to be “the only game in town” when it comes to trying to understand the age limits on children’s ability to comprehend ideas of various levels of complexity. One problem with it is that it is not always straightforward to determine the arity of a concept, or number of ideas that must be associated. For example, a young child might understand the concept big(elephant) and then might be able to infer that elephants are bigger than dogs, without being able to use the concept of bigger than in a consistent manner more generally. The concept from Miller (1956) that items can be combined using knowledge to form larger chunks also applies to associations, and it is not clear how to be sure that the level of complexity actually is what it is supposed to be. Knowledge allows some problems to be solved with less working memory requirement.

Explanations Based on Knowledge

It is beyond question that knowledge increases with age. Perhaps this knowledge increase is the sole reason for developmental change in working memory, it has been argued. Chi (1978) showed that children with an expertise in the game of chess could remember chess configurations better than adults with no such expertise. The expert children presumably could form larger chunks of chess pieces, greatly reducing the memory load. Case, Kurland, and Goldberg (1982) gave adults materials that were unfamiliar and found that both the speed of item identification and the memory span for those materials closely resembled what was found for 6-year-olds on familiar materials. The implication was that the familiarity with the materials determines the processing speed, which in turn determines the span.

Explanations Based on Processing Speed and Strategies

Case et al. (1982) talked of a familiarity difference leading to a speed difference. Others have suggested that, more generally, speed of processing increases with age in childhood and decrease again with old age (e.g., Kail & Salthouse, 1994 ). This has led to accounts of working memory improvement based on an increased rate of covert verbal rehearsal ( Hulme & Tordoff, 1989 ) or increased rate of attentional refreshing ( Barrouillet, Gavens, Vergauwe, Gaillard, & Camos, 2009 ; Camos & Barrouillet, 2011 ). At the lower end of childhood, it has been suggested on the basis of various evidence that young children do not rehearse at all ( Flavell, Beach, & Chinsky, 1966 ; Garrity, 1975 ; Henry, 1991 ) or do not rehearse in a sufficiently sophisticated manner that is needed to assist in recall ( Ornstein & Naus, 1978 ). When rehearsal aloud is required, the result suggest that the most recently rehearsed items are recalled best ( Tan & Ward, 2000 ).

This view that rehearsal is actually important has been opposed recently. It is not clear that rehearsal must be invoked to explain performance ( Jarrold & Citroën, 2013 ) and if rehearsal takes place, it is not clear exactly what the internal processes are (e.g., cumulative repetition of the list? Repetition of each item as it is presented?).

In the case of using attention to refresh information, an interesting case can be made. Children who are too young (about 4 years of age and younger) do not seem to use attention to refresh items. For them, the limit in performance depends on the duration of the retention interval. For older children and adults, who are able to refresh, it is not the absolute duration but the cognitive load that determines performance ( Barrouillet et al., 2011 ). The “phase change” in performance that is observed here with the advent of refreshing is perhaps comparable to the phase change that is seen with the advent of verbal rehearsal ( Henry, 1991 ), though the evidence may be stronger in the case of refreshing.

Re-assessment of Capacity Accounts

We have seen that there are multiple ways in which children’s working memory performance gets better with maturity. There are reasons to care about whether the growth of capacity is primary, or whether it is derived from some other type of development. For example, if the growth of capacity results only from the growth of knowledge, then it should be possible to teach any concept at any age, if the concept can be made familiar enough. If capacity differences come from speed differences, it might be possible to allow more time by making sure that the parts to be incorporated into a new concept are presented sufficiently slowly.

We have done a number of experiments suggesting that there is something to capacity that changes independent of these other factors. Regarding knowledge, relevant evidence was provided by Cowan, Nugent, Elliott, Ponomarev, and Saults (1999) in their test of memory for digits that were unattended while a silent picture-rhyming game was carried out. The digits were attended only occasionally, when a recall cue was presented about 1 s after the last digit. The performance increase with age throughout the elementary school years was just as big for small digits (1, 2, 3), which are likely to be familiar, as for large digits (7, 8, 9), which are less familiar. Gilchrist, Cowan, and Naveh-Benjamin (2009) further examined memory for lists of unrelated, spoken sentences in order to distinguish between a measure of capacity and a measure of linguistic knowledge. The measure of capacity was an access rate, the number of sentences that were at least partly recalled. The measure of linguistic knowledge was a completion rate, the proportion of a sentence that was recalled, provided that at least part of it was recalled. This sentence completion rate was about 80% for both first and sixth grader children, suggesting that for these simple sentences, there was no age difference in knowledge. Nevertheless, the number of sentences accessed was considerably smaller in first-grade children than in sixth-grade children (about 2.5 sentences vs. 4 sentences). I conclude, tentatively at least, that knowledge differences cannot account for the age difference in working memory capacity.

We have used a different procedure to help rule out a number of factors that potentially could underlie the age difference in observed capacity. It is based on a procedure that has been well-researched in adults ( Luck & Vogel, 1997 ). On each trial of this procedure, an array of simple items (such as colored squares) is presented briefly and followed by a retention interval of about 1 s, and then a single probe item is presented. The latter is to be judged identical to the array item from the same location, or a new item. This task is convenient partly because there are mathematical ways to estimate the number of items in working memory ( Cowan, 2001 ). If k items are in working memory and there are N items in the array, the likelihood that the probed item is known is k/N , and a correct response can also come from guessing. It is possible to calculate k , which for this procedure is equal to N ( h-f ), where h refers the proportion of change trials in which the change was detected (hits) and f refers to the proportion of no-change trials in which a change was incorrectly reported (false alarms).

One possibility is that younger children remember less of the requested information because they attend to more irrelevant information, cluttering working memory (for adults, cf. Vogel, McCollough, & Machizawa, 2005 ). To examine this, Cowan, Morey, AuBuchon, Zwilling, and Gilchrist (2010) presented both colored circles and colored triangles and instructed participants to pay closer attention to one shape, which was tested on 80% of the trials in critical blocks. When there were 2 triangles and 2 circles, memory for the more heavily-attended shape was better than memory for the less-attended shape, to the same extent in children in Grades 1–2 and Grades 6–7, and in college students. Yet, the number of items in working memory was much lower in children in Grades 1–2 than in the two older groups. It did not seem that the inability to filter out irrelevant information accounted for the age difference in capacity.

Another possibility is that in Cowan et al. (2010) , the array items occurred too fast for the younger children to encode correctly. To examine this, Cowan, AuBuchon, Gilchrist, Ricker, & Saults (2011) presented the items one at a time at relatively slow, a 1-item-per-second rate. The results remained the same as before. In some conditions, the participant had to repeat each color as it was presented or else say “wait” to suppress rehearsal; this articulatory manipulation, too, left the developmental effect unchanged. It appears that neither encoding speed nor articulation could account for the age differences. So we believe that age differences in capacity may be primary rather than derived from another process.

Age differences in capacity still could occur because of age differences in the speed of a rapid process of refreshment, and from the absence of refreshment in young children ( Camos & Barrouillet, 2011 ). Alternatively, it could occur because of age differences in some other type of speed, neural space, or efficiency. This remains to be seen but at least we believe that there is a true maturational change in working memory capacity underlying age differences in the ability to comprehend materials of different complexity. This is in addition to profound effects of knowledge acquisition and the ability to use strategies.

The use of strategies themselves may be secondary to the available working memory resources to carry out those strategies. According to the neoPiagetian view of Pascual-Leone and Smith (1969) , for example, the tasks themselves share resources with the data being stored. Cowan et al. (2010) found that when the size of the array to be remembered was large (3 more-relevant and 3 less-relevant items, rather than 2 of each) then young children were no longer able to allocate more attention to the more-relevant items. The attentional resource allocated to the items in the array was apparently deducted from the resource available to allocate attention optimally.

In practical terms, it is worth remembering that several aspects of working memory are likely to develop: capacity, speed, knowledge, and the use of strategies. Although it is not always easy to know which process is primary, these aspects of development all should contribute in some way to our policies regarding learning and education.

Working Memory and Learning

In early theories of information processing, up through the current period, working memory was viewed as a portal to long-term memory. In order for information to enter long-term memory in a form that allows later retrieval, it first must be present in working memory in a suitable form. Sometimes that form appears modality-specific. For example, Baddeley, Papagno, and Vallar (1988) wondered how it could be that a patient with a very small verbal short-term memory span, 2 or 3 digits at most, could function so well in most ways and exhibit normal learning capabilities. The answer turned out to be that she displayed a very selective deficit: she was absolutely unable to learn new vocabulary. This finding led to a series of developmental studies showing that individual differences in phonological memory are quite important for differences in word-learning capability in both children and adults ( Baddeley, Gathercole, & Papagno, 1998 ; Gathercole & Baddeley, 1989 , 1990 ).

Aside from this specific domain, there are several ways in which working memory can influence learning. It is important to have sufficient working memory for concept formation. The control processes and mnemonic strategies used with working memory also are critical to learning.

Working Memory and Concept Formation

Learning might be thought of in an educational context as the formation of new concepts. These new concepts occur when existing concepts are joined or bound together. Some of this binding is mundane. If an individual knows what the year 1776 means and also what the Declaration of Independence is (at least in enough detail to remember the title of the declaration), then it is possible to learn the new concept that the Declaration of Independence was written in the year 1776. Other times, the binding of concepts may be more interesting and there may be a new conceptual leap involved. For example, a striped cat is a tiger. As another simple example, to understand what a parallelogram is, the child has to understand what the word parallel means, and further to grasp that two sets of parallel lines intersect with one another. The ideas presumably must co-exist in working memory for the concept to be formed.

For the various types of concept formation, then, the cauldron is assumed to be working memory. According to my own view, the binding of ideas occurs more specifically in the focus of attention. We have taken a first step toward verifying that hypothesis. Cowan, Donnell, and Saults (in press) presented lists of words with an incidental task: to report the most interesting word in each presented list. Later, participants completed a surprise test in which they were asked whether pairs of words came from the same list; the words were always one or two serial positions apart in their respective lists, but sometimes were from the same list and sometimes from different lists. The notion was that the link between the words in the same list would be formed only if the words had been in the focus of attention at the same time, which was much more likely for short lists than for long lists. In keeping with this hypothesis, performance was better for words from short lists of 3 items (about 59%) than for words from lists of 6 or 9 items (about 53%). This is a small effect, but it is still important that there was unintentional learning of the association between items that were together in the focus of attention just once, when there was no intention of learning the association.

The theory of Halford et al. (1998) may be the best articulated theory suggesting why a good working memory is important for learning. (In this discussion, a “good” working memory is simply one that can keep in mind sufficient items and their relations to one another to solve the problem at hand, which may require a sufficient combination of capacity, speed, knowledge, and available strategies.) More complex concepts require that one consider the relationship between more parts. A person’s working memory can be insufficient for a complex concept. It may be possible to memorize that concept with less working memory, but not truly to understand the concept and work with it. Take, for example, use of the concept of transitivity in algebra. If a+b=c+d and c+d=e , then we can conclude that a+b=e because equality is transitive. Yet, a person who understands the rules of algebra still would not be able to draw the correct inference if that person could not concurrently remember the two equations. Even if the equations are side by side on the page, that does not mean that they necessarily can be encoded into working memory at the same time, which is necessary in order to draw the inference. Lining up the equations vertically for the learner and then inviting the learner to apply the rule by rote is a method that can be used to reduce the working memory load, perhaps allowing the problem to be solved. However, working out the problem that way will not necessarily produce the insight needed to set up a new problem and solve it, because setting up the problem correctly requires the use of working memory to understand what should be lined up with what. So if the individual does not have sufficient working memory capacity, a rote method of solution may be helpful for the time being. More importantly, though, the problem could be set up in a more challenging manner so that the learner is in the position of having to use his or her working memory to store the information. By doing so, the hope is that successful solution of the problem then will result in more insight that allows the application of the principles to other problems. That, in fact, is an expression of the issues that may lead to the use of word problems in mathematics education.

Working Memory and Control Processes

Researchers appear to be in fairly good agreement that one of the most important aspects of learning is staying on task. If one does not stick to the relevant goals, one will learn something perhaps, but it will not be the desired learning. Individuals who test well on working memory tasks involving a combination of storage and processing have been shown to do a better job staying on task.

A good experimental example of how staying on task is tied to working memory is one carried out by Kane and Engle (2003) using a well-known task designed long ago by John Ridley Stroop. In the key condition within this task, one is to name the color of ink in which color words are written. Sometimes, the color of ink does not match the written color and there is a tendency to want to read the word instead of naming the color. This effect can be made more treacherous by presenting stimuli in which the word and color match on most trials, so that the participant may well lapse into reading and lose track of the correct task goal (naming the color of ink). What that happens, the result is an error or long delay on the occasional trials for which the word and ink do not match. Under those circumstances, the individuals who are more affected by the Stroop conditions are those with relatively low performance on the operation span test of working memory (carrying out arithmetic problems while remembering words interleaved with those problems).

In more recent work, Kane et al. (2007) has shown that low-span individuals have more problems attending in daily life. Participants carried devices that allowed them to respond at unpredictable times during the day, reporting what they were doing, what they wanted to be doing, and so on. It was found that low-span individuals were more likely to report that their minds were wandering away from the tasks on which they were trying to focus attention. This, however, did not occur on all tasks. The span-related difference in attending was only for tasks in which they reported that they wanted to pay attention. When participants reported that they were bored and did not want to pay attention, mind-wandering was just as prevalent for high spans as for low spans.

Although this work was done on adults, it has implications for children as well. Gathercole, Lamont, and Alloway (2006) suggest that working memory failures appear to be a large part of learning disabilities. Children who were often accused of not trying to follow directions tested out as children with low working memory ability. They were often either not able to remember instructions or not able to muster the resources to stick to the task goal and pay attention continually, for the duration needed. Children with various kinds of learning and language disability generally test below grade level on working memory procedures, and children with low working memory and executive function don’t do well in school (e.g., Sabol & Pianta, 2012 ).

Of course, central executive processes must do more than just maintain the task goal. The way in which information is converted from one form to another, the vigilance with which the individual searches for meaningful connections between elements and new solutions, and self-knowledge about what areas are strong or weak all probably play important roles in learning.

Working Memory and Mnemonic Strategies

There also are special strategies that are needed for learning. For example, a sophisticated rehearsal strategy for free recall of a list involves a rehearsal method that is cumulative. If the first word on the list is a cow, the second is a fish, and the third a stone, one ideally should rehearse cumulatively: cow…cow, fish….cow, fish, stone … and so on ( Ornstein & Naus, 1978 ). Cowan, Saults, Winterowd, and Sherk (1991) showed that young children did not carry out cumulative rehearsal the way older children do and could not easily be trained to do so, but that their memory improved when cumulative rehearsal was overtly supported by cumulative presentation of stimuli.

For long-term learning, maintenance rehearsal is not nearly as effective a strategy as elaborative rehearsal, in which a coherent story is made on the basis of the items; this takes time but results in richer associations between items, enhancing long-term memory provided that there is time for it to be accomplished (e.g., Craik & Watkins, 1973 ).

In addition to verbal and elaborative rehearsal, Barrouillet and colleagues (2011) have discussed attentional refreshing as a working-memory maintenance process. We do not yet know what refreshing looks like on a moment-to-moment basis or what implications this kind of maintenance strategy has for long-term learning. It is a rich area for future research.

The most general mnemonic strategy is probably chunking ( Miller, 1956 ), the formation of new associations or recognition of existing ones in order to reduce the number of independent items to keep track of in working memory. The power of chunking is seen in special cases in which individuals have learned to go way beyond the normal performance. Ericsson, Chase, and Faloon (1980) studied an individual who learned, over the course of a year, to repeat lists of about 80 digits from memory. He learned to do so starting with a myriad of athletic records that he knew so that, for example, 396 might be recoded as a single unit, 3.96 minutes, a fairly fast time for running the mile. After applying this intensive chunking strategy in practice for a year, a list of 80 digits could be reduced to several sub-lists, each with associated sub-parts. The idea would be that the basic capacity has not changed but each working-memory slot is filled with quite a complex chunk. In support of this explanation, individuals of this sort still remain at base level (about 7 items) for lists of items that were not practiced in this way, e.g., letters. (For a conceptual replication see Ericsson, Delaney, Weaver, & Mahadevan, 2004 ; Wilding, 2001 )

Although we cannot all reach such great heights of expert performance, we can do amazing things using expertise. For example, memorization of a song or poem is not like memorization of a random list of digits because there are logical connections between the words and between the lines. A little working memory then can go a long way.

The importance of a good working memory comes in when something new is learned, and logical connections are not yet formed so the working memory load is high. When there are not yet sufficient associations between the elements of a body of material, working memory is taxed until the material can be logically organized into a coherent structure. Working memory is thought to correlate most closely with fluid intelligence, the type of intelligence that involves figuring out solutions to new problems (e.g., Wilhelm & Engle, 2005 ). However, crystallized intelligence, the type of intelligence that involves what you know, also is closely related to fluid intelligence. The path I suggest here is that a good working memory assists in problem-solving (hence fluid intelligence); fluid intelligence and working memory then assist in new learning (hence crystallized intelligence).

Working Memory and Education

We have sketched the potential relation between working memory and learning. How is that to be translated into lessons for education? There is a large and diverse literature on this topic. As a starting point to illustrate this diversity, I will describe the chapters chosen for the book, Working memory and education ( Pickering, 2006 ). After an introductory chapter on working memory (A. Baddeley), the book includes two chapters on the relation between working memory and reading (one by P. de Jong and another by K. Cain). There is a chapter on the relation between working memory and mathematics education (R. Bull and K.A. Espy), learning disabilities (H.L. Swanson), attention disorders (K. Cornish and colleagues), and deafness (M. Keehner & J. Atkinson). Other chapters cover more general topics, including the role of working memory in the classroom (S. Gathercole and colleagues), the way to assess working memory in children (S. Pickering), and sources of working memory deficit (M. Minear and P. Shah). It is clear that many avenues of research relate working memory to education, and I cannot travel along all of them in this review.

To organize a diverse field, what I can do is to distinguish between several different basic approaches have been tried. First, one can try to teach to the level of the learner’s working memory. The points described in the article up to this point should be kept in mind when one is trying to discern and understand what a particular learner can and cannot do. Second, one can try to use training exercises to improve working memory, which, investigators have hoped, would allow a person to be able to learn more and solve problems more successfully. The message I would give here is to be wary, given the rudimentary state of the evidence in a difficult field and the plethora of companies selling working memory training exercises. Third, one might contemplate the role of working memory for the most critical goals of education, in a broad sense. These topics will be examined one at a time.

Teaching to the Level of Working Memory

The classic adaptation of education to cognitive development and the needs of learning has been to try to adjust the materials to fit the learner. For example, there has been considerable discussion of the need to delay teaching concepts of arithmetic at least until the children understand the basic underlying concept of one-to-one correspondence; that is, the idea that there are different numbers in a series and that each number is assigned to just one object, in order to count the objects (e.g., Gelman, 1982 ). Halford et al. (2007) provide rough description of what complexity of concepts to expect for each age range, based on working-memory limits (see also Pascual-Leone & Johnson, 2011 ).

There also are individual differences within an age group in ability that affect how the materials are processed. For example, individuals lower in working memory may prefer to take in information using a verbatim, shallow, or surface processing strategy, rather than try to extract the gist (for one relevant investigation, albeit with mixed results, see Kyndt, Cascallar, & Dochy, 2012 ). The enjoyment of technological presentations may be greater in students with better abilities in the most relevant types of working memory (e.g., Garcia, Nussbaum, & Preiss, 2011 ). I would note that the educational enterprise requires that the teacher must decide whether it is best to allow the learner to use a favored strategy, which may be influenced by the student’s ability level, or whether it is possible in some cases to instill a more effective strategy even if it does not come naturally to the student.

Sweller and colleagues ( Sweller, 2011 ; Sweller, van Merrienboer, & Paas, 1998 ) have summarized a body of research literature and a theory about the role of cognitive load in learning and education. Their cognitive load theory is “a theory that emphasizes working memory constraints as determinants of instructional design effectiveness” ( Sweller et al., 1998 ). The theory distinguishes between an intrinsic cognitive load that comes from material to be learned and an extraneous cognitive load that should be kept small enough that the cognitive resources of the learner are not overly depleted by it. The theory is importantly placed in an evolutionary framework that I will not describe (though above I mentioned the theory’s incorporation of the distinction between biologically primary and secondary information). This theory has the advantage of being rather nuanced in that many ramifications of cognitive load are considered. With too high a cognitive load, one runs the risk of the student not being able to follow the presentation, whereas with too low a cognitive load, one runs the risk of insufficient engagement. In future, it might be possible to refine the predictions for classroom learning by combining cognitive load theory with theories of cognitive development, which make some specific predictions about how much capacity is present at a particular age in childhood (e.g., Halford et al., 2007 ). For further discussion of the theory as applied specifically to multimedia, see Schüler, Scheiter, and Genuchten (2011) . Issues arise as to how printed items are encoded (visually, verbally, or both) and how much the combination of verbal and visual codes in multimedia should be expected to tax a common, central cognitive resource and therefore interfere with one another, even when they are intended to be synergic. Both in cognitive psychology and in education, these are key issues currently under ongoing investigation.

An advantage of multimedia and computerized instruction is the possibility of adjusting the instruction to the student’s level. This might be done partly on the basis of success; if the student succeeds, the materials can be made more challenging whereas, if the student fails, the materials can be made easier. One potential pitfall to watch for is that, while some students will want to press slightly beyond their zone of comfort and will learn well, others will want an easy time, and may choose to learn less than they would be capable of learning. One way to cope with these issues is through computerized instruction, but with a heavy dose of personal monitoring and adjustment to make sure that the task is sufficiently motivating for every student.

One factor that makes it difficult to teach to the students effectively is that the working memory demands of language production do not always match the demands of the recipients’ language comprehension. Consequently, when one is speaking or writing for didactic purposes, one must be careful to consider not only one’s own working memory needs, but also those of the listener or reader. There are several obstacles in this regard. Slevc (2011) showed that speakers tend to blurt out what is most readily available in working memory. He used situations that were to be described verbally by the participant, e.g., A pirate gave a book to the monk . If one piece of information had already been presented, it was more likely to be described first. For example, if the monk had been presented already but not the book, the participant was more likely to phrase the description differently, as A pirate gave the monk a book . This assignment of priority to given information is generally appropriate, given that the speaker and listener (or writer and reader) share the same given information. In this case, though, Slevc shows that the tendency to describe given information first was diminished when the speaking participant was under a working memory load. In a didactic situation such as giving a lecture, it thus seems plausible that the memory load inherent in the situation (remembering and planning what one wants to say in the coming segments of a lecture) may cause the lecturer sometimes to use awkward grammatical structure. Moreover, as mentioned above, learning to speak or write well requires that one bear in mind possible difference between what one knows as the speaker (or writer) and what the listener (or reader) knows at key moments. For example, if one says, “Marconi was the inventor of the modern radio,” then, by the time the full topic of the sentence is known, the name is most likely no longer in the listener’s or student’s working memory. If, however, one says, “The modern radio was invented by a man named Marconi,” the context is set up first, making it easier to retain the name. Bearing in mind what the listener or reader knows and does not yet know is likely to be important both for educators in their own speaking and writing, and also in order to teach students how to speak and write effectively.

Working Memory Training

A much more controversial approach is to use training regimens to improve working memory, thereby improving performance on the educational learning tasks that require working memory (e.g., Klingberg, 2010 ). It is controversial partly because many people have spent a great deal of money purchasing such training programs before the scientific community has reached an agreement about the efficacy of such programs.

Doing working memory training studies is not easy. One needs a control group that is just as motivated by the task as the training group but without the working memory training aspect. The training task must be adaptive (with rewards for performance that continues to improve with training) and a non-adaptive control group does not adequately control arousal and motivation. Some task that is adaptive but involves long-term learning instead of working memory training may be adequate. Several large-scale reviews and studies have suggested that working memory training sometimes improves performance on the working memory task that is trained, but does not generalize to reasoning tasks that must rely on working memory (in adults, Redick et al., 2013 , and Shipstead, Redick, & Engle, 2012 ; in children, Melby-Lervåg & Hulme, 2013 ). In somewhat of a contrast, other reviews suggest that the training of executive functions (inhibiting irrelevant information, updating working memory, controlling attention, etc.) does extend at least to tasks that use similar processes ( Diamond & Lee, 2011 ) and some basically concur also for working memory ( Chein & Morrison, 2010 ). So there is an ongoing controversy, even among those who have written meta-analyses and reviews of research.

One might ask how it is possible to improve working memory without having the effect of improving performance on other tasks that rely on working memory. This can happen because there are potentially two ways in which training can improve task performance. First, working memory training theoretically might increase the function of a basic process, much as a muscle can be strengthened through practice. (Or at least, individuals might learn that through diligent exertion of their attention and effort, they can do better.) That is presumably the route hoped for in training of working memory or executive function. Second, though, it is possible for working memory training to result in the discovery of a strategy for completing the task that is better than the strategy used initially. This can improve performance on the task being trained, but the experience and the strategy learned may well be irrelevant to performance on other educational tasks, even those that rely on working memory. This route might be expected if, as I suspect, participants typically look for a way to solve a problem that is not very attention-demanding, unless the payoff is high.

If there is successful working memory training, another issue is whether training is capable of producing super-normal performance or whether it is mostly capable of rectifying deficiencies. By way of analogy, consider physical exercise. If a person is already walking 6 miles a day, there might be little benefit to the heart of adding aerobic exercise. Similarly, if a person is already highly engaged in the environment and using attention control often and effectively during the day, there might be little benefit to the brain of adding working memory exercises. It remains quite conceivable, though, that such exercises are beneficial to certain individuals who are under-utilizing working memory. Nevertheless, as Diamond and Lee (2011) points out, there might be social or emotional reasons why this is the case and such factors would need to be addressed along with, or in some cases instead of, working memory training per se.

Working Memory and the Ultimate Goals of Education

What is the difference between learning and education? This is a question that has long been asked (for a history of the early period of educational psychology in the United States, for example, see Hall, 2003 ). Do children learn better when they are fed the information intensively, or allowed to explore the material? Should all children be expected to learn the same material, or should children be separated into different tracks and taught the information that is thought to help them the most in their own most likely future walks of life?

A fundamental difference between learning and education, many would agree, is that education should facilitate the acquisition of skills that will promote continued learning after the student leaves school. Of course, after the student leaves school, a major difference is that there is no teacher to decide what is to be learned, or how. Therefore, what seems to be most important, many would agree, is critical thinking skills. There is some sentiment that these skills can be trained (although for an opposing view see Tricot & Sweller, in press ). For example, Halpern (1998 p. 449) suggests the following emphases for training critical thinking: “(a) a dispositional component to prepare learners for efforiful cognitive work, (b) instruction in the skills of critical thinking, (c) training in the structural aspects of problems and arguments to promote transcontextual transfer of critical-thinking skills, and (d) a metacognitive component that includes checking for accuracy and monitoring progress toward the goal.” Although I could find few well-controlled, peer-reviewed studies supporting the notion that it is possible to train critical thinking skills, optimistic evidence is beginning to roll in. For example, Shim and Walczak (2012) found that professors asking challenging questions resulted in more improvement in both subjective and objective measures of critical thinking. The objective measure required that students clarify, analyze, evaluate, and extend arguments, and increased 0.55 standardized units for every 1-unit increase in the rating of challenging questions asked. The gain was much stronger in students with high pretest scores in critical thinking. Halpern et al. (2012) have designed a computerized module to train critical thinking skills and obtained very encouraging initial results, with well-controlled training experiments in progress according to the report.

One can then ask, to what extent is the training of these higher-level skills dependent on the student’s working memory ability? The association is likely to be substantial, given the high correlation between working memory and reasoning ability even among normal adults ( Kyllonen & Christal, 1990 ; Süβ, Oberauer, Wittmann, Wilhelm, & Schulze, 2002 ). There is the possibility that training working memory will in some way improve reasoning and vice versa, though most would agree at this point that the case has not yet been completely made (e.g., Jaeggi & Buschkuehl, 2013 ; Shipstead et al., 2012 ).

A current interest of mine is to understand how fallacies in reasoning might be related to fallacies in working memory performance. There appear to be some similarities between the two. One of the best-known reasoning fallacies is confirmation bias. In a key example ( Wason & Shapiro, 1971 ) participants are given a set of cards laid on the table, each having a letter on one side and a number on the other, and are asked which cards must be turned over to assess a rule (e.g., If a card has a vowel on one side, it has an even number on the other side ). Participants get that they must turn over the cards that can either confirm or disconfirm the rule (in the example, the cards showing vowels). They often fail to realize that they must also turn over cards that can only disconfirm the rule. In the example, one must turn over cards with odd numbers because the rule is disconfirmed if any of those cards have a vowel on the other side. In contrast, cards that can only confirm the rule are irrelevant. (One should not turn over cards with even numbers because the rule is technically not disconfirmed no matter whether there is a consonant or vowel on the other side.)

Chen and Cowan (in press) found performance on a working memory task that closely resembles confirmation bias. In one procedure, a spatial array of letters was presented on each trial, followed by a set of all of the letters at the bottom of the screen and a single location marked; the task was to select the correct letter for the marked location. In another procedure, the spatial array of letters was followed by a single letter from the array at the bottom of the screen and all of the locations marked; the task was to select the correct location for the presented letter. When working memory does not happen to contain the probed item, these procedures allow the use of disconfirming information. In the first task, for example, a participant might reason as follows: The letters were K, R, Q, and L. I know the locations of only R and L and neither of them match the probed location. Therefore, I know that the answer must be K or Q and I will guess randomly between them . That would be comparable to using disconfirming evidence. The pattern of data, however, did not appear to indicate that kind of process. Instead, participants answered correctly if they knew the probed item and otherwise guessed randomly among all of the other choices, without using the process of elimination. A mathematical model that assumed the latter process showed near-perfect convergence in capacity between the procedures described above and the usual change-detection procedure. If we instead assumed a mathematical model of performance in which disconfirming evidence was used through the process of elimination, there was no such convergence between the procedures.

So in reasoning and in working memory, processing tends to be inefficient, and it remains to be seen whether it can be meaningfully improved in terms of eliminating confirmation bias. Perhaps people with insufficient working memory or intelligence will always be stuck in such inefficient reasoning and there is nothing we can do. Arguing against that pessimistic view, however, is the recent finding ( Stanovich, West, & Toplak, 2013 ) that the tendency to evaluate evidence more favorably when it agrees with one’s own view occurs across the board and is not correlated with intelligence, and presumably therefore not correlated with working memory either. One might be able to train individuals to make the best use of the working memory they have without worrying about increasing the basic capacity of working memory, either by training critical thinking skills (Halpern, 1989) or by instilling expertise ( Eriksson et al., 2004 ).

Working memory is the retention of a small amount of information in a readily accessible form, which facilitates planning, comprehension, reasoning, and problem-solving. When we talk of working memory, we often include not only the memory itself, but also the executive control skills that are used to manage information in working memory and the cognitive processing of information. Theoretically, there is still uncertainty about the basic limitations on working memory: are they limitations on concurrent holding capacity, mnemonic processing speed, duration of retention of information before it decays, or just the same sorts of interference properties that apply to long-term memory? While these basic issues are debated and empirical investigations continue, there is much greater agreement about what results are obtained in particular test circumstances; the results of working memory studies seem rather replicable, but small differences in method produce large differences in results, so that one cannot assume that a particular working memory finding is highly generalizable.

For learning and education, it is important to take into account the basic principles of cognitive development and cognitive psychology, adjusting the materials to the working memory capabilities of the learner. We are not yet at a point at which every task can be analyzed in advance in order to predict which tasks are doable with a particular working memory capability. It is possible, though, to monitor performance and keep in mind that failure could be due to working memory limitations, adjusting the presentation accordingly. Keeping in mind the limitations of working memory of listeners and readers could easily help to improve one’s lecturing and writing styles. I hope that awareness of working memory leads to a world in which we are all more tolerant of one another’s inability to understand perfectly, are more humble and less arrogant, and are better able to communicate, educate one another, and reach common ground.

Acknowledgment

This work was completed with support from NIH grant R01-HD21338.

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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.

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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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Working memory: a complete guide to how your brain processes information, thinks and learns.

How do you keep everything in mind when solving tough problems? When you read a book, listen to a podcast or have a conversation–how does your brain hold onto all the information?

The answer is something psychologists call working memory .

Unlike long-term memory, which I’ve covered in-depth here , working memory isn’t about remembering the past. Instead, it’s about holding together the present in your mind so you can learn, make decisions and solve problems.

Working memory is essentially your mental bandwidth. If you have a good working memory, or can use yours more effectively, you can think and learn better. Thus, understanding this important facet of your mind is essential for anyone who wants to perform better in work, school and life.

To give you that understanding, I’ve collaborated again with Jakub Jilek , who has his masters in cognitive science and is currently studying for his PhD. We’ve put together a full guide to explaining what your working memory is, how it works, and most of all–how you can apply simple methods to think and learn better.

Side note: Like our last guide, this one is substantial. If you’d like to go over it as a PDF instead of just reading along here (either to print or to save for later) you can join my newsletter and I’ll send you a free copy of the PDF:

Just want the advice? Jump ahead to Summary of Key Methods and Techniques !

Table of Contents

• Working Memory

How Working Memory Underpins Your Ability to Learn

How can you measure your working memory, are all sounds equally harmful to learning, does music affect everyone the same way, how to use sound to boost your learning, strategies for improving your visuospatial working memory, how to use visualization and drawing to improve learning, the hidden costs of multi-tasking, who is affected by multi-tasking, how badly designed textbooks split your attention, how to use chunking as a mnemonic technique, chunking works by reducing memory load, how experts use chunks, build chunks with pre-training, reduce intrinsic load with segmenting and worked-examples, reduce extrinsic load with visually simple textbooks and a goal-free approach, how to optimize cognitive load, why does anxiety burden our working memory, how you can overcome anxiety, summary and conclusion, citations and references, what is working memory the four components underlying your ability to think and learn.

What is working memory? The easiest way to understand working memory is by visualizing it as a carpenter’s workbench: [ 1 ] The carpenter temporarily places tools and materials on the workbench as she builds new products. The workbench has a small size – only a few items can be placed on it at once.

Similarly, you temporarily store information in your working memory when you’re solving a problem or making a decision. Working memory also has a small capacity – it can only hold a few items at once.

However, the workbench is not just for keeping materials in one place. It’s a workspace – the carpenter uses it to combine different materials to create new products. Similarly, working memory is not just a simple storage. Working memory enables you to generate new thoughts, change them, combine them, search them, apply different rules and strategies to them, or do anything else that helps you navigate your life.

By enabling all of these functions, working memory underpins your thinking, planning, learning and decision-making.

Scientists have developed various models of working memory. In this guide, we will draw on the most popular model, which has been developed by Alan Baddeley . [ 2 ] According to this model, working memory can be divided into four components:

The first component is called the phonological loop. It’s essentially a storage of sounds – it allows you to temporarily memorize digits, words and sentences (by the way they sound).

The second component is called the visuospatial sketchpad. As the name suggests, the sketchpad stores two- and three-dimensional images of objects.

The third component is the central executive. Its main responsibility is directing attention and manipulating information.

Using our workbench analogy, you could think of the the phonological loop and the visuospatial sketchpad as two different vises that hold materials in one position. Each vise can hold a different kind of material (such as wood or metal). Similarly, the phonological loop can hold sounds and the visuospatial sketchpad can hold images.

You could think of the central executive as the carpenter herself. The carpenter decides which tools and materials to use in the same way as the central executive decides which things to pay attention to. She shapes metal and wood by using chisels, saws and drills to create a new product such as a chair. Similarly, the central executive re-arranges ideas and applies the rules of grammar, logic or algebra to come up with a solution to a problem or make a decision.

Baddeley’s model also has a fourth component (“episodic buffer”) which we won’t cover here because it’s not so well researched as the other three components.

You may have also heard of the term “short-term memory”. Scientists currently use this term when they talk about a simple temporary storage (but not manipulation) of information, [ 3 ] which can be of any kind (visual or auditory). The term “working memory” is used to talk about the whole storage and manipulation system.

To give you a quick recap, here’s the three main parts of working memory:

• Phonological loop – stores sounds including words, digits, sentences
• Visuospatial sketchpad – stores images of objects
• Central executive – directs attention and manipulates information

In this guide we’ll look at all these three components and see how they impact on your learning. In addition, we’ll cover another three important topics, which are closely connected to working memory:

• Chunking – the compression of information
• Cognitive load – the processing demands placed on working memory
• Anxiety – the culprit behind problems with working memory

One quick thing before we get started. If you’re interested in this stuff, you’ll probably enjoy my weekly newsletter, devoted to the art of learning, productivity and getting more from life. If you sign-up below, I’ll send you a free rapid-learning ebook:

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Why Working Memory Matters

Working memory is a key aspect of intelligence. [ 4 ] Much of your learning depends on your working memory.

Think of the last time you followed a hard class. In the beginning, you might have kept up fine. But eventually it became harder and harder to understand what the professor was saying. Even though you tried your best to pay attention, you left feeling confused and frustrated.

It turns out that the culprit is likely an overloaded working memory [ 5 ] (read Summary and Conclusion for other possibilities). The study material required your working memory to process too much new information at the same time. As a result, the system became overwhelmed and broke down.

Even if you don’t regularly attend confusing lectures, understanding how your working memory functions is essential for learning better.

In order to learn, you first must comprehend. [ 6 ] [ 7 ] To do this, your working memory is always involved:

Your phonological loop must keep track of the sounds of the words you read or hear. Your central executive must constantly update these sequences as you go along. Finally, these meanings need to be integrated so you can understand everything. If any of these processes fail, you’ll get lost and confused.

Solving problems is also essential to learning. [ 8 ] Once again, your working memory is working hard.

Consider trying to solve the problem of adding two numbers:

87 + 65 = ?

Most of us learn how to add numbers like these in grade school (the solution is 152). Despite the simplicity, however, there’s a lot of complicated cognition to pull off this calculation. [ 9 ] [ 10 ]

Your visuospatial sketchpad first has to store a visual representation of the symbols. Your central executive has to apply the rules of addition and store the intermediate steps (e.g. 80 + 60). Finally, your phonological loop has to maintain the subvocal instructions to control the operation (“add eighty and sixty” etc.). [ 11 ] If any of these problems fail the result is, again, confusion and getting lost.

Besides comprehension and problem-solving, working memory underpins many other learning skills. Note-taking [ 12 ] requires you to quickly store and process what is has just been said while simultaneously processing what is being said right now.

It shouldn’t surprise you now that working memory capacity has been found to be significantly connected to reading comprehension [ 13 ][ 14 ] , maths [ 15 ] and problem-solving. [ 16 ] Students who have a better working memory enjoy better grades. [ 17 ] Most importantly, higher working memory capacity predicts better learning outcomes and achievement. [ 18 ][ 19 ][ 20 ]

Can You Improve Your Working Memory

You’ve probably heard of memory experts who can remember astonishingly long sequences of random digits or words. For example, Rajan Mahadevan is able to correctly retrieve a staggering 31,811 digits of the mathematical constant pi (long-term memory). He can also remember up to 63 randomly presented digits or words (working memory). [ 21 ] Another mnemonist, Suresh Kumar Sharma, holds the Guinness world record for managing to recite pi to 70,030 digits without making any mistakes. [ 22 ]

You may be thinking that it’s impossible to achieve such amazing feats unless you’re born naturally gifted.

Although both of these mnemonists have likely had an above-average working memory since childhood, genetic predispositions are by no means the whole story. If these champions were naturally blessed with a fantastic working memory, then we would expect them to excel in all tasks requiring working memory, right?

Researchers decided to test this idea. [ 23 ] Instead of digits or words, they gave Rajan Mahadevan series of symbols (such as !, @, *, +, etc.). Can you guess how many symbols Rajan managed to remember?

To everyone’s surprise, Rajan could only keep 6 of these symbols in his working memory – the same as an average university student.

When interviewing these and other mnemonists, scientists found that they had devoted extensive time of practice to hone their memory. What’s more important, they use highly sophisticated and refined versions of mnemonic techniques such as the method of loci or the story method. [ 24 ]

All these results suggest that working memory is (to some degree) a skill like any other – if you practice it, you can improve it.

While the jury is still out whether and to what degree it’s possible to improve the core processes of working memory, [ 25 ] scientists have discovered many techniques that help you make your working memory more efficient and effective. In the following sections we’ll describe how you can apply these techniques to boost your comprehension and problem-solving skills.

If you set out to improve your working memory, it can be useful to know how you can measure it. Scientists distinguish between short-term memory capacity and working memory capacity. [ 26 ]

Short-term capacity is simply your ability to temporarily store of small amounts of information. [ 27 ] This information can be digits, letters, words, symbols, pictures, scenes, or anything else. Short-term memory span is the number of items that one can store in their short-term memory.

Would you like to know your digit span?  Try this online test . Scroll down the webpage, uncheck “sound enabled”, set the starting sequence length to 3 and click start. Do this at least three times and then compute the average, which will be your digit span. You can also click “repeat” if you want to repeat a sequence with the same number of digits.

The average human span is 4 items, [ 28 ] although the exact number depends on the type of items. People can typically remember more letters than words and more digits than letters. The average digit span is 7 digits.

Working memory capacity is your combined ability to store and manipulate information. It’s traditionally measured with complex span tasks (such as the operation span) and the famous n-back. These tests can’t be taken online, but you can download them here .

Phonological Loop: How Music Disrupts Your Studies

Phonological loop is the first kind of short-term memory storage which stores sounds. Being able to have a conversation, listen to music and understand a lecture all depend on your phonological loop.

As you read these lines, your phonological loop is working at every moment. It uses subvocalisation (your internal voice) to translate visual information (digits, letters, words and sentences) into auditory information, which is then processed to extract meaning. [ 29 ]

If the subvocalisation process is disrupted, it will be hard to maintain information in your phonological loop. As a consequence, your comprehension will suffer. To see this on yourself, try the following experiment:

If you haven’t already done so, measure your digit span . After you’ve done that, measure your digit span again. This time, however, firstly start playing a favorite song of yours that contains lyrics (it shouldn’t be a purely instrumental piece). Set the volume to a comfortable level (not too quiet but not too loud). What is your digit span now?

It’s likely that your digit span is now one or more digits lower. [ 30 ] This is because the music interfered with the subvocalisation process, which was thus less effective at encoding information in your phonological loop.

Many studies have shown that listening to many kinds of sounds and music can have a profoundly negative impact on your working memory, reading comprehension and mathematical problem-solving. [ 31 ] For instance, one study has shown that students who revise in a quiet environment later perform 60% better in an SAT comprehension test than their peers who listen to music (with lyrics). [ 32 ]

However, different kinds of sounds have different effects. Firstly, the detrimental effect is much stronger with vocal music compared to instrumental music. One study showed that students who revised without music were 10% better than students who revised while listening to instrumental music. [ 33 ]

Secondly, it doesn’t matter if you don’t understand the language. Foreign language also impairs working memory. [ 34 ] Thirdly, although even pure tones can disrupt performance, the tones have to fluctuate. If the pure tone has a constant pitch, it doesn’t have a harmful effect on memory. [ 35 ]

Listening to music doesn’t affect everyone in the same way. In general, individuals with a high working memory capacity are more resistant to the harmful effects of music. [ 36 ]

However, students are very bad at predicting what effect music has on their performance. Interestingly enough, the students who prefer listening to music while studying are also those whose reading comprehension is most likely to suffer due to interference from music. [ 37 ]

Why do so many students listen to music although it impairs their learning? Why do they even feel that they benefit from this? We believe that the reason for this might be twofold:

Firstly, music could help reduce anxiety and help one calm down, which may be beneficial for studying. [ 38 ] Secondly, music could drown out even more disrupting external noise, which might actually help to protect working memory.

Interestingly, although white noise seems to worsen the performance of students with normal attention, it can actually improve the performance of students with attention problems. [ 39 ]

In general, we would recommend that you avoid listening to music while studying (especially vocal music). It’s important that you study in a quiet environment where no-body is speaking or making any other noise. The exception to this rule is when you’re preparing for an exam that will take place in a noisy environment. In this case, it’s beneficial to spend some time revising in a noisy environment (to see why, check our Complete Guide on Memory, section “ Context-dependence ” ).

If you cannot revise in a quiet environment, the best way to reduce noise is by using earplugs. Alternatively, a not too harmful option is to listen to white noise (check out the plethora of white-noise nature sounds on YouTube). If you do have to listen to music, go for instrumental music.

The first strategy to improve your learning is by protecting your phonological loop from interfering sounds. Scientists have found yet another strategy that significantly boosts learning and that also makes use of sound.

In an intriguing study, students had to memorize lists of words. [ 40 ] The first group read the words aloud, the second listened to a recording of their own voice reading the words, the third group listened to someone else, while the fourth group studied the words in silence. Interestingly, the first group showed the best performance (20% better than the fourth group), followed by the second, third and fourth group.

The advantage of reading aloud over reading silently for subsequent memory performance is called the “production effect”. [ 41 ]

Scientists believe that producing words makes them more distinctive than reading them silently because you additionally use your vocal cords and facial muscles. [ 42 ]

To harness the production effect, however, you shouldn’t read aloud all of your study material. Distinctiveness is relative – a word read aloud will stand out in the context of silently-read words but it won’t stand out if all other words are also read aloud. [ 43 ] Therefore, to get the most benefit, we recommend that you use the production effect only for a selection of the most important information.

In summary, we recommend the following:

• Ideally, avoid noise during learning and don’t listen to any kind of music
• The best way to down out noise is by using earplugs (or listening to white noise)
• If you do have to listen to music (because it helps you calm down for instance), choose instrumental music with no lyrics
• Only apply this to a selection of the most important concepts / information
• If you read aloud everything, it won’t work

Visuospatial Sketchpad: Upgrade Your Imagination

Visuospatial sketchpad is the second kind of short-term memory storage. It stores two- or three-dimensional objects and their positions in space.

The visuospatial sketchpad is essential for understanding mathematical, science, technology and engineering subjects. Visuospatial working memory capacity in childhood reliably predicts mathematical achievements in adolescence even when other factors such as intelligence are accounted for. [ 44 ]

In a stunning study, researchers from Berkley examined the visuospatial skills of engineering students. [ 45 ] They found that the men performed on average nearly 10% better than women in various tasks such as mental rotation of objects. The researchers later interviewed experienced engineers and asked them to share their strategies for solving visuospatial problems.

On the basis of these strategies, they designed a visuospatial training program. All women who had low scores were invited to attend the program. Interestingly, after only 3 hours of training, there were no longer any significant differences between men and women.

This study demonstrates how the use of appropriate strategies can substantially (and quickly) help your visuospatial sketchpad. Which strategies are the best? In the study mentioned above, the researchers found that different engineers used different strategies that achieved the same result.

Therefore, there seems to be no single “right” strategy for approaching visuospatial problems. However, you can develop your own strategy. We’re going to show you how to do it on the following task: Have a look at the picture below and try to find the folded cube which cannot be made from the unfolded cube (there’s only one).

Before we give you the correct answer, think of the strategy that you used. There are two broad strategies for these kinds of problems. A holistic strategy consists of firstly folding the cube, then rotating it mentally as a whole and comparing it with the folded cubes. This is the most working-memory demanding strategy. In contrast, an analytic strategy consists of noticing the relationships between the patterns in a step-by-step way. Let’s walk through an analytic strategy:

If you look at the first folded cube, you can ask yourself: If the white cross is above the black x , can the five dots be on the right?

Then look at the unfolded cube. Visualize the unfolded cube in such a way that the white cross is above the black x .

From this position you can see easily that the first folded cube is the same as the unfolded cube.

As an alternative, you could “unfold” the cubes first, possibly even draw them unfolded. Then rotate and compare the unfolded cubes to see if they fit.

If you apply one of these strategies to the remaining three cubes, you’ll see that it’s the fourth cube that doesn’t fit.

If you can use the holistic approach straightaway then it’s likely that your visuospatial sketchpad has a high capacity. If not, then you can benefit from using a more piecemeal approach. The whole idea is to offload information from your working memory – to break down the task into smaller, more manageable pieces and to store intermediate steps on paper. This way you can achieve the same result as someone with a high working memory capacity, albeit perhaps more slowly.

The visuospatial sketchpad is useful not only for visuospatial problems. The phonological loop and the visuospatial sketchpad are largely independent of each other. [ 46 ] Therefore, you can use your visuospatial sketchpad to help your phonological loop and vice versa.

A beautiful demonstration of how the visuospatial sketchpad can help the phonological loop was carried out by scientists who examined Japanese experts on mental calculation. [ 47 ] These experts have a very high digit span (16 number) and they can quickly subtract and add up numbers having up to 9 digits. Where does their miraculous ability come from? Through practice, these experts have learnt to construct a “virtual” abacus in their minds that they use to make calculations.

While a mental abacus is probably no longer needed in the age of computers, you can use visualization in other ways: If you’re going shopping and you want to remember shopping list, you can chunk it into one picture. For instance, you could imagine peppers, milk, chicken and mustard as mustard-covered chicken, swimming in a bowl of cereal and surrounded by peppers.

Visualization strategies can be beneficial for your reading comprehension as well. In an interesting study, researchers asked students to read a scientific text from chemistry. [ 48 ] One group of students was given no strategy, one group was asked to focus on the text (summarize and find the main points), whereas the last group was asked to use the drawing-construction strategy (draw molecules and their bonds). At the end of the study session, students were assessed with a test.

One would expect that focusing on the text, finding its main points and being able to summarize it, should be the key ingredients of reading comprehension. However, the results showed the exact opposite. The drawing students outperformed the no-strategy students by 30%. What’s more, summarizing actually worsened the performance of the text-focused group compared to the control group.

Although the drawing-construction strategy improves students’ comprehension of particular scientific texts, [ 49 ] research has yet to show whether it generalizes to all subjects and all kinds of texts. You need to experiment with yourself to find out how when drawing is useful and when it isn’t.

Moreover, the quality of drawings is essential for the technique to be effective. [ 50 ] This means that your drawings need to be a faithful representation of the text’s contents, correctly capturing the relationships between different concepts.

Therefore, it undoubtedly takes some practice to master the skill of visualization. Nevertheless, although drawing is not an out-of-the-box strategy, if done well, it can become a powerful technique in you learning arsenal.

• Don’t worry if you have problems with visuospatial tasks – it’s mostly a matter of choosing the right strategy.
• Break down complex tasks into small components.
• Offload the results of intermediate steps onto paper.
• This strategy can make you process information more deeply.

Central Executive: How to Concentrate Your Mind Easily

The central executive is the third component of working memory. The central executive has many functions. Here we’ll focus on allocation of attention and manipulation of information.

Selective attention is the ability to direct cognitive resources to things which are relevant to the task at hand and to filter out everything else. [ 51 ]

Trying to pay attention to multiple things at the same time (multi-tasking) is generally harmful to performance. Using our workbench analogy from the beginning, imagine that we asked our carpenter to chisel, saw and drill several different pieces of wood at the same time. The result of such effort would likely be a shoddy product. Unsurprisingly, a wealth of studies have shown the detrimental effects of multi-tasking on comprehension, learning and students’ grades. [ 52 ]

As a matter of fact, “multi-tasking” is a bit of a misnomer. [ 53 ] True multi-tasking is quite rare because it is very difficult to pay attention to two things at the same time. Multi-tasking typically consist of switching back and forth between multiple tasks, rather than simultaneously focusing on several tasks.

Multi-tasking is inefficient because each switch that you make incurs a cost. [ 54 ] If you’re oscillating between reading your notes and checking your phone, for instance, each switch takes some time and energy – you have shift your goals (“Now I want to do this instead of that”) and re-activate the rules for the activity you’re switching to (read a paragraph – type a response).

Although one task switch may only take a few seconds (and seem insignificant), all the myriad switching done within one day can add up to a substantial amount of time and eat away at your productivity.

The negative effect of multi-tasking can be quite insidious. In a series of studies, [ 55 ] researchers had students read a text passage and assessed their comprehension with tests. Some students also carried out an interruption task (solving a math problem between each paragraph).

Researchers found that the interruption had no effect on students’ knowledge (they could correctly answer questions despite the interruption). However, when global comprehension was assessed (the text’s theme and tone, the author’s goals and morale), the interruption worsened performance by as much as 30%.

This study nicely demonstrates that you might feel that multi-tasking is not affecting your performance based on the fact that you remember everything from the text easily. However, your comprehension, which requires synthesizing information from different parts of the text, could still suffer.

It may come as a surprise, but multi-tasking may not always harmful. What matters is whether the two tasks employ the same cognitive processes. [ 56 ] This happens, for instance, when you’re watching television while reading your notes. Doing these two activities simultaneously is going to interfere with your comprehension as both of these activities compete for access to your phonological loop.

However, reading a book while sitting on the train or practicing flashcards while commuting, will likely not substantially impair your comprehension. (Scott: I was listening to music while drawing the images for this post, but I never listen to music while writing.)

Research has also shown that individuals with a high working memory capacity are more resistant to the negative effects of multi-tasking (especially if the secondary task is not too demanding). [ 57 ] Therefore, if you have a high working memory capacity, you might be able to do multi-tasking without substantially hurting your performance.

Multi-tasking is a form of dividing your attention. Besides different activities (like watching TV and reading notes), attention can also be divided among different study materials. If you have multiple source materials which you have to look at while studying, then your comprehension will suffer. This is called the split-attention effect. [ 58 ]

As a demonstration, we’ve prepared two tasks from geometry. You don’t need to solve the tasks, just have a look at them. Both tasks ask you to do exactly the same thing (calculate two angles), however, each task is presented differently. Which of the two tasks seems easier?

The correct answers are 60°and 120° degrees, respectively. Did you find the second task easier to understand?

Whereas the first task was presented with separate textual and graphical information, the second task featured information integrated into a coherent whole.

The first task placed an unnecessary load on the central executive, which had to shift attention between the text and the picture and combine it together to enable understanding. This was essentially extra manipulation of information that had nothing to do with solving the actual task. In contrast, the second task freed up cognitive resources that could be instead devoted to solving task.

Researchers have found that if study material is presented in an integrated format, then comprehension improves dramatically (one study has reported a 30% improvement compared to split-attention format [ 59 ] ). This effect has been found for all kinds of subjects, including geometry, programming, geography and engineering. [ 60 ]

Consider another example. The simple arrangement and distance of words on vocabulary flashcards can make a significant difference to your retention:

Compared to the second example, the first example places a demand on your central executive, which has to figure out the way from the Chinese character to its phonic equivalent. Indeed, presenting flashcards like the second example substantially improves later recall. [ 61 ]

You may not be able to select your study material or perhaps there are no textbooks / lecture notes available which present material in an integrative way. However, you need not depend on the particular way your study material is structured. When taking notes, make sure that you have all information in one place. Stick to the rule “one concept must fit on one page”. If you can’t fit one concept on one page then you need to break it down into smaller concepts.

Pay attention to how your study material is structured. If you have to study from multiple sources (several textbooks / notebooks), it might be a good idea to combine the information and put it all into one place (by re-writing or photocopying for instance). If this is too cumbersome, then drawing a structure, a concept map or an outline of what you’re studying should also help.

If you have difficulty understanding a concept, re-draw graphs and re-write your notes so that everything is integrated in one place. This way you will free up precious working memory resources, which you’ll be able to devote to comprehension.

• Avoid multi-tasking and interruptions even if you feel that it’s not affecting you – the negative effect can be well hidden from your sight
• Multi-tasking will not affect your learning and performance only if the two or more activities that you do simultaneously don’t share the same working memory resources (e.g. practicing flashcards while commuting)
• When studying, put all information relevant to one concept into one place to prevent divided attention
• Try to find study materials which feature integrated information (graphs and text combined together rather than presented separately)
• If necessary, re-draw or photo-copy different parts of your notes/textbooks/lecture notes so that everything is integrated
• Design your own study materials (like flashcards) in an integrative way to boost your memory

Chunking – the secret to expertise

For two years, researchers followed a single student of average intelligence and short-term memory capacity. [ 62 ] Every day, the student had to listen to sequences of digits. While at the start, he could only recall 4 digits, by the end of the study, he managed to correctly remember a series of 80 digits.

When interviewing the student, the researchers found that the he was a competitive runner. When hearing the sequences of digits, the student transformed every 4 digits into a running time (e.g. 3492 was transformed to 3 minutes and 49.2 seconds). In this way, he effectively compressed 4 units of information into 1 unit of information.

The process of compressing information is called “chunking”. To see how chunking works, you can try the following little experiment: [ 63 ]

1) Look at these letters for 10 seconds and try to memorize as many of them as possible, while covering the rest of the page:

2) Now do the same thing with these letters:

The chances are that you probably couldn’t recall all of the letters from the first list, but you could easily recall all of the letters from the second list. What’s going on here?

You may have noticed that the letters in both lists are the same, only arranged differently. However, while in the first list you had to memorize 12 letters (which is way above the average short-term memory span), in the second list you were not memorizing letters at all. Instead, you memorized 4 syllables (FRAC-TO-LIS-TIC).

The key idea behind chunking is that you group the underlying items by some sort of meaning or structure. The group then becomes a single unit (=chunk). Although our short-term memory can only hold 4 chunks at a time, these chunks can be fairly complex.

You can easily use chunking to memorize phone numbers, passwords or PIN codes. Simply divide the given sequence into chunks containing the maximum of 4 items each. For instance, to remember the phone number 743293045, you could split the number with dashes like this: 743-293-045. This way, you effectively have to remember only 3 chunks of information, instead of 9 separate digits. If you’re interested in more advanced chunking methods for long sequences of numbers, have a look at the phonetic-number system .

You can also use chunking to boost your learning. A useful chunking technique is organization. Organization is when you categorize unstructured study material into meaningful groups. For example, you can group foreign language vocabulary based on topics, similar meanings (synonyms) or similar pronunciation.

The structure can also be more complex (hierarchical). For instance, you can study chemical elements grouped by their various properties. Research shows that people can memorize up to twice as many hierarchically organized items than unorganized items. [ 64 ]

Chunking reduces the load on working memory because it replaces the items in your working memory with items from your long-term memory. [ 65 ] To see how it works, try the following experiment:

Memorize the following list of 5 words (while covering the rest of the page). You have 5 seconds:

large, run, tremble, believe, fish, series

How many words did you remember?

Now memorize another list of 5 words. You have 5 seconds:

besar, berlari, gemetar, percaya, ikan, siri

How many words did you remember now? Although the second list contained the same number of words (which had the same meaning and almost the same number of letters in total), you probably remembered fewer words from the second list than from the first list. How is this possible?

As an English speaker, you probably knew all the words from the first list. However, unless you speak Malay, you didn’t know any of the words from the second list. The first list was easier precisely because you could use your pre-existing knowledge of English vocabulary stored in your long-term memory. You simply “downloaded” each word from your long-term memory as a chunk.

In contrast, since you couldn’t retrieve the Malay words from your long-term memory, you could only “download” smaller chunks from your long-term memory – syllables or letters. As a result, there were many more pieces of information that had to be stored in your working memory from the second list.

Researchers have found that although humans have a very limited working memory capacity, their long-term memory capacity can be astonishingly high. In one study, [ 66 ] scientists asked subjects to look at 2500 pictures for three seconds each. After that, they asked them about the details of selected pictures such as the positions of objects, their shape and color. Surprisingly, subjects were 90% accurate at remembering the details of the pictures.

Therefore, the most powerful way that you can free up your working memory capacity is by drawing on your long-term memory resources. The more knowledge you have stored in your long-term memory, the less information you need to process with your working memory and the easier will it be to understand your study material and solve problems.

Chunking is the secret behind acquiring mastery in any subject [ 67 ] (alternative explanations have also been proposed – see Ericsson’s long-term working-memory hypothesis). [ 68 ] This is because any kind of complex skill is essentially a huge chunk containing a large number of nested chunks.

Consider playing the piano: Playing the piano consists of many skills, such as sight reading, finger techniques, understanding of rhythm, pushing the pedals, and many others. Each of these skills also consists of further sub-skills. For example, sight reading requires the knowledge of keys, notes, scales and various musical symbols denoting rhythm and volume. For a novice player, doing all of these things at the same time is an impossible task. And yet expert musicians can play complex pieces with little effort, even by sight-reading only.

Expert musicians can play the piano with little effort precisely because they do not have to retrieve each individual skill separately. This would overload their working memory and make performance impossible. Instead, they retrieve one large chunk from their long-term memory that contains all of these sub-skills “compressed” within it. This saves precious working memory resources which can be devoted to processing other information such as sight-reading.

Therefore, to master any subject, you need to firstly build solid foundations of the basics (the elementary chunks). Only then can you attempt to form increasingly complex chunks.

Understanding chunking can help you with your comprehension and problem-solving skills. If you’re experiencing difficulty understanding your study material or cannot solve a problem, then it’s likely that your working memory is overloaded. [ 69 ] Working memory becomes overloaded if it has to process too much information at the same time. This typically happens when you don’t have sufficient knowledge of the prerequisites.

If this is the case, practicing your target skill (e.g. solving many differential equations) likely won’t be of much help or it will be inefficient. A far superior strategy is to firstly identify the underlying sub-skills (arithmetic, algebra) that you may be lacking and master these first. This way you can save yourself substantial amounts of time and effort.

If you have difficulty understanding something, firstly identify the underlying chunks and store them into your long-term memory. This technique is called pre-training. [ 70 ] Pre-training is very effective for all kinds of subjects. As an illustration, consider the following study: [ 71 ]

Students were taught about the car-braking system. One group was firstly introduced to the names of each component (the pedals, the piston, the master cylinder) and their locations. Only once they had mastered the individual components were they taught about their behavior and how they worked together to achieve braking. In contrast, the second group of students was taught all information at once.

Although both groups were exposed to identical material, the pre-training procedure led to substantially better comprehension and recall (up to 30%) than presenting all information at the same time.

You can use pre-training to approach any study material. Firstly, identify the key concepts and vocabulary. Secondly, use the internet or any other resource to find simple definitions. Thirdly, begin to explore how the concepts relate to one another.

In all courses and textbooks it’s often the case that each new lecture (or chapter) requires some knowledge of the previous chapters. If you’re having difficulty understanding a lecture, you might be missing something from the previous lectures and you need to re-study it.

If you have trouble solving mathematical problems, it’s likely that you don’t have properly formed chunks for the underlying operations. For instance, it’s difficult to solve a differential equation without the knowledge of algebra (re-arranging equations) and arithmetic (addition, subtraction, multiplication and division). If you master the underlying sub-skills first, then mathematics will be much easier.

Our general recommendations are the following:

• Use chunking to compress information so that you can remember more.
• For instance, you can group foreign language vocabulary by topics, similar meanings, or similar pronunciation.
• You can do this with pre-training (pre-studying the definitions and meanings of concepts before your lecture or before you read a textbook)
• If you don’t understand something, try to identify what exactly you’re having a problem with and study this first
• Firstly master the underlying sub-skills and then practice your target skill to save time and energy

Cognitive load: the culprit behind learning difficulties

So far we’ve talked about various ways how you can reduce the load placed on your working memory in order to boost your comprehension and problem-solving skills. Scientists have developed a theory of cognitive load which explores in detail the different kinds of load that can be placed on working memory. [ 72 ]

Cognitive load is defined as the effort used by the working memory system to process information. The main idea of the cognitive load theory is that working memory capacity is limited. If the working memory resources that are needed to process information are greater than your capacity, then you will fail to understand the information. Using our workbench analogy, this would be comparable to our carpenter trying work with too many tools and materials at the same time, which would start falling off the workbench as a result.

There are three types of cognitive load: Intrinsic, extrinsic and germane. All types of load are additive – their sum makes up the overall load on your working memory.

Intrinsic load is associated with the task, it’s basically the level of difficulty of the subject. As an illustration, compare the obvious differences in difficulty between solving a simple calculation (2 + 2 = ?) and a complicated equation like the one below:

Intrinsic load is fixed for a particular kind of task and for each individual (given their current level of abilities). High intrinsic load can be beneficial as it stimulates effective learning. However, if it exceeds your working memory resources, it can impair your learning.

One way you can reduce intrinsic load is by gaining more knowledge of the underlying chunks (we covered this in the previous section). Another way is to reduce the complexity of the material.

You can reduce complexity by segmenting and sequencing. [ 73 ] Instead of reading a textbook chapter all at once, split it up into bite-sized chunks. Separate long passages of text graphically (e.g. draw a line to create new paragraphs if necessary). When you’ve done this, study the information step by step. If you come across a graph or a passage that you cannot understand, cover up parts of it and focus on smaller elements. The less information you need to process at one time, the easier it will be to understand it.

Another great way to reduce complexity is by going through worked-example problems. [ 74 ] Worked examples guide you through each step of problem-solving and teach you the model that you can then apply on new problems. Worked examples are especially useful during early stages of learning. Many textbooks now have worked examples.

However, be careful – badly designed worked examples are useless. Good worked-examples have clear language and graphics and are easy to follow. If your worked example is difficult to understand – it causes high cognitive load – then you need to find a different one.

In contrast with intrinsic load, extrinsic load is associated with the way the study material is presented. If you’re experiencing difficulty understanding something, maybe it’s because of high extrinsic load.

Perhaps your lecturer is difficult to understand. Maybe your textbook / lecture notes are not well written and understandable. Do not feel that you are stuck with whatever your course offers to you. Devoting some time before you start learning something to find high-quality materials is definitely a worthwhile investment.

One reason why study materials may impose a high cognitive load is because they contain a lot of redundant information. Authors of textbooks often try to make them visually appealing by including lots of unnecessary decorations, photos and graphics. The rule of thumb is that the more visually appealing a textbook is, the higher extrinsic load it will impose. Unless they are used for explanation of study material, graphics only burden the visuospatial sketchpad.

Another way that you can reduce extrinsic load is by approaching problems in a goal-free way. In the geometrical example that we presented in section “visuospatial sketchpad”, the goal was to compute the angles alpha and beta. A goal-free approach to this problem would be to calculate any kind of angle and as many angles as possible in any order. [ 75 ]

If you have a given goal, then you have to process the goal, the problem givens and the difference between the two simultaneously. In a goal-free approach, you focus only on the current state and how to get to the next state. As a result, the extrinsic load on your working memory is decreased.

The goal-free approach is particularly suitable for math and programming. [ 76 ] For instance, if you have a programming assignment, instead of trying to solve it straight-away, firstly explore its components. Play with different functions – see what kind of inputs they take and what outputs they produce. Similarly, if you’re solving a math or geometry problem such as the one above, don’t try to reach the goal immediately. Instead, explore the problem and calculate different things in a step-by-step way.

The third type of cognitive load is called germane. Germane load is the effort that you have to make to construct integrated chunks of information (called schemas) from the concepts in your study material. To successfully learn something, you need to devote some of your working-memory resources to germane load. To achieve this, you need to minimize the level of extrinsic load and optimize the level of intrinsic load (i.e. find the right level of difficulty).

How do you know which type of cognitive load is causing you problems? Researchers have developed a simple questionnaire that reliably tells apart between different types of cognitive load. [ 77 ]

In essence, if you feel that the activity, the covered concepts, formulas or definitions are complex, then high intrinsic load is likely the culprit. However, if you feel that the instructions/explanations are unclear or ineffective, or full of unclear language, then the problem lies with high extrinsic load.

• If your study material feels too complex, then you need to reduce your intrinsic load
• If your study material feels unclear or confusing, then you need to reduce your extrinsic load
• To reduce intrinsic load, use segmenting and sequencing or find some worked examples
• To reduce extrinsic load, find study materials with clear language and modest graphics, and approach solving problems in a goal-free way

Anxiety: how to turn it into excitement

So far we have covered various things that can place a load on your working memory and impair your comprehension and problem-solving skills. It turns out that one of the major causes of cognitive load is anxiety.

Try to imagine how well our carpenter would perform if she felt anxious. Her hands would probably tremble and she would have difficulty concentrating. In fact, she might even drill a hole in the wrong place or saw off an important part, spoiling the final product.

Anxiety is especially harmful to mathematics, [ 78 ] but it can also worsen performance in other subjects, such as biology. [ 79 ] One would expect that individuals with an already low working memory capacity would be most affected by anxiety. However, the opposite is true. High working memory capacity individuals use high-demand strategies for solving problems. Performance pressure takes away the resources that these individuals need to solve problems.

Scientists believe that when you are anxious, your working memory is preoccupied with anxious thoughts. [ 80 ] So instead of the task at hand, your short-term storage is filled with irrelevant information. In particular, verbal rumination (sub-vocally repeating anxious thoughts) interferes with the phonological loop. Anxious thoughts can be associated with images, which occupy the visuospatial sketchpad. Moreover, if you pay attention to these anxious thoughts, this also places demands on the central executive.

Math anxiety could be a learned phenomenon. Researchers believe that we learn anxiety from our parents when they help us with homework. [ 81 ] They give out verbal and non-verbal signals that math is something difficult and anxiety-provoking.

Unfortunately, math anxiety is also caused directly by teachers. Teachers who are themselves insecure about their mathematical ability (it’s surprising how many of them are!) [ 82 ] tend to give harsh feedback, use defective teaching methods and spread the toxic belief that some people can never become good at math. All of these factors have a severe impact on students’ mathematical abilities and self-confidence.

It may be impossible to change your school or university teacher. However, in the age of internet you’re not bound to one incompetent teacher. For math in particular, you can check online courses and websites (the best one is the Khan Academy) which have excellent teachers who will guide you through the whole curriculum step-by-step, with a calm reassuring voice and completely for free. Don’t let your teacher spoil your experience with math – ignore them, take the initiative and make a switch to someone better.

In addition, you can take steps to effectively address your own anxiety. It turns out that the effect that anxiety has on your performance largely depends on the beliefs you have about it. If you believe that math anxiety will harm you, then you will perform worse. On the other hand, if you believe that math anxiety will help you perform better, then it won’t impact on you. [ 83 ]

One way to overcome anxiety is therefore through a technique called “cognitive reappraisal”. [ 84 ] Try to think of anxiety not as anxiety, but as excitement. These two emotions are both arousing and seem to be quite similar physiologically. Researchers have found that although such a simple reframing of your emotions does nothing to change your anxiety level or bodily response (heart rate, etc.), it improves your performance.

You can reframe your mindset by using subvocalization or speaking aloud to yourself. In particular, you can override the anxious thoughts by repeating excitement-promoting mantras (“I’m excited”, “Get excited”). Often it’s as simple as that. Even reading an article about the benefits of short-term stress can help.

Another techniques that has been found to be effective is expressive writing (or journaling ). [ 85 ] If you are anxious about a test or an exam, write about your thoughts and your worries. By writing these down, you can effectively offload them from your working memory. Expressive writing is especially effective if you elaborate in detail on your deep feelings and what in particular is causing you to feel anxious (which aspects of math or math tests you’re most afraid).

• If your teacher is math-anxious, ignore them and find a better teacher online (e.g. the Khan academy)
• Use cognitive reappraisal and subvocalization to transform anxiety into excitement (“I’m excited”)
• Use expressive writing to offload your worries from memory onto paper

Let’s recap what we’ve learned!

Your working memory is the workbench of your mind. It keeps track of what you’re seeing, hearing, thinking and imagining while allowing you to work with that to produce long-term memories and solutions.

The most popular scientific model has four components of which we reviewed the most well-studied three:

• Phonological Loop . Keeps track of what you’ve just heard. Also used to subvocalize thoughts, while reading, speaking or thinking.
• Visuospatial Sketchpad . Keeps track of pictures and spatial information.
• Central Executive . Allocates attention and manipulates information, just like a carpenter on the workbench.

The most important finding about working memories is that they are limited. The average person can only hold 4-7 pieces of information at a time .

The flip-side of this is that we can chunk information. By combining complex information into recognizable chunks, even super complicated things can fit onto your mental workbench.

To make best use of your working memory:

• Avoid music and distracting sounds while doing mentally demanding work and studying.
• Emphasize the most important information by speaking it aloud.
• Use visual mnemonics to keep track of more ideas at once.
• Visualization can improve studying over merely summarizing for some subjects. Try to apply your imagination more when you study.
• If you struggle with a problem, break it into simpler parts.
• Mastery comes from chunking–building up stored patterns so complex things become simple.

In addition to the components of working memory, we talked about three other issues. Chunking, cognitive load and anxiety.

Cognitive load determines a lot of what makes something confusing or difficult. (Attention and specific learning disabilities, can also be factors, however.) In particular there are three types of cognitive loads:

• Intrinsic load. The difficulty of the idea itself.
• Extrinsic load. Difficulties due to poor presentation/instruction.
• Germane load. The effort required to make new chunks and remember.

You can mitigate intrinsic load by pre-training . Breaking down a complex subject into simple parts, which you master first before moving on.

You can ease extrinsic load by finding good resources for learning, or reorganizing confusing ones .

Finally anxiety has a big impact on working memory. By crowding out the information you need to process, distracting thoughts can make it very hard to perform. Try reframing your anxiety as excitement, seeking confident instructors and journaling your thoughts to make it easier.

Scott Young

I’m a writer, programmer, traveler and avid reader of interesting things. For the last ten years I’ve been experimenting to find out how to learn and think better. More About Scott

Jakub Jílek

Jakub recently graduated from Cognitive and Decision Sciences at University College London and he’s currently starting a PhD in Cognitive Neuroscience. More About Jakub

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[71] Mayer, R. E., Mathias, A., & Wetzell, K. (2002). Fostering understanding of multimedia messages through pre-training: Evidence for a two-stage theory of mental model construction. Journal of Experimental Psychology: Applied, 8, 147–154.

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[74] Sweller, J. (2011). Cognitive Load Theory. (P. Ayres, S. Kalyuga, S. (Online Service), & L. (Online Service), Eds.) (1.). New York, NY : Springer New York.

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[76] Maulidya, S. R., Hasanah, R. U., & Retnowati, E. (2017). Can goal-free problems facilitating students’ flexible thinking? AIP Conference Proceedings, 1868(1), 50001. https://doi.org/10.1063/1.4995128

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Working Memory Concept Essay

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Introduction

The function of working memory, false memory, works cited.

In 1968, Atkinson and Shiffrin introduced the multi-store model of memory. It quickly gained a lot of attention from the scientific community, which proceeded to further research the multi-store model. Over time, however, some of the concepts of this model were found to be too simplistic and not representative of reality. As an alternative, Baddeley and Hitch created a new concept called working memory. This paper will provide an overview of the working memory model, including its general process of functioning and how false memories can form during its operation.

The idea of short-term memory proposed by Atkinson and Shiffrin was unitary and far too limited in its flexibility. With a capacity for only five to nine items over the duration of 30 seconds, it could not be applied to more complex cases and did not explain many of the issues that can arise when a person’s short-term memory is damaged. To address these problems, Baddeley and Hitch proposed a non-unitary model of working memory.

This model consists of three primary components: the central executive, the phonological loop, and the visuo-spatial sketchpad. The central executive is responsible for allocating the processing resources of the brain between the loop and the sketch pad. The phonological loop represents the person’s inner voice and analyzes phonological information. The visuo-spatial sketchpad processes visual and spatial information, as well as representing the inner eye of the person. The working memory model proposes that the functions of the phonological loop and the visuo-spatial sketchpad are performed simultaneously and independently of each other (D’Esposito and Postle 139).

The central executive, as the name implies, is the primary component of the working memory system; every other component is subservient to it. Not much is known about the central executive, but it is known that it is responsible for coordinating secondary components, monitoring their work, and relaying their results to long-term memory. The central executive chooses which component should process the received information, and it is also responsible for prioritizing one memory activity over another. Because of its importance, any impairment to the central executive can lead to significant issues with short-term memory.

The name “phonological loop” comes from the system’s ability to store and repeat the sound of the inner voice. It usually operates for around 20-30 seconds and is capable of storing larger amounts of information as long as it is grouped into meaningful chunks of about five to eight digits. The loop itself consists of two subcomponents. The first is the phonological store, which represents the inner ear. It can store portions of sounds from around the person, as well as those recalled from long-term memory. The second is called the articulatory control process, and it represents the inner voice.

This process is what causes people to hear their own voice while thinking. The visuo-spatial sketchpad is in charge of the visual short-term memory. It can recall both two-dimensional and three-dimensional images. This model of memory proposes an explanation for memory disorders in which a person has an impaired digit span but is able to use visual short-term memory without problems. Later, the original model was updated to include another component called the “episodic buffer,” a feature that is also controlled and monitored by the central executive. Its function is to act as backup storage that communicates between long-term memory and other components of the model (Ma et al. 348).

Memories are often not an accurate representation of the real events. This fact is due to a vast variety of reasons, from the presence of strong emotions to different memory disorders. Memories that elicit a strong emotional response are called flashbulb memories. These emotions often distort the person’s memory of the event, making it less accurate. For example, the terrorist attacks on the World Trade Center tend to elicit memories affected by emotions, which even with yearly reminders from media do not retain their accuracy over time (Hirst et al. 620).

Personally, I have experienced the phenomenon of possibly false memories when I was almost hit by a bus six years ago. I was returning home after going out with my friends when I carelessly started to cross the road without looking. A large white bus stopped only a few inches from me, honking its horn loudly and stopping with a screech of the tires. The problem is that even though I remember this happening, there is no guarantee that the bus was white or that it was actually that close to me. After six years, my mind could have unintentionally changed the details of the story.

Memories can not only become distorted but can also be completely false. Cryptomnesia causes a person to substitute his or her personal memory with someone else’s. For example, a person can come to believe that he or she invented something because they learned how it was invented long ago. False memories can be implanted by suggestion or may be created due to brain damage or immature frontal lobes.

Repressed memory therapy can lead to the creation of false memories as well. Some of the more common false memories come from the human desire to create a consistent reconstruction of past events in their lives. I have experienced this type of false memory as well. When I think about my time in school, there is a relatively small number of memories that are clear. Because this period covers everything from primary to high school, there is no way for me to accurately remember all the events in order. This is where false memories come in; I might misremember when I last saw classmates who I did not know too well.

I often misremember when certain classes were introduced into my curriculum, and I even find myself having false memories of when I met my school friends. With no record of most events, I can only hope to reconstruct these events with some portion of accuracy. The problem of false memories also makes eyewitnesses less reliable. Coupled with the effects of flashbulb memory and personal biases, there is a high chance of receiving false information from eyewitnesses. Other factors like sleep deprivation, intoxication, and stress can also lead to inaccurate memories and even the creation of false ones. Such things as illusion and dreams can become a part of memory, especially during intoxication (Conway and Loveday 579).

There are still things that the scientific community does not understand about memory. Despite the great research that has gone into this topic, information on the working memory system is lacking. False memories are common and can be created under a variety of conditions. However, with the help of new neuroscientific technology, scientists should be able to gain new insights into this topic and, over time, gain a better understanding of how memory actually works.

Conway, Martin A., and Catherine Loveday. “Remembering, Imagining, False Memories & Personal Meanings.” Consciousness and Cognition , vol. 33, no. 5, 2015, pp. 574-581. Web.

D’Esposito, Mark, and Bradley R. Postle. “The Cognitive Neuroscience of Working Memory.” Annual Review of Psychology , vol. 66, no. 1, 2015, pp. 115-142. Web.

Hirst, William et al. “A Ten-Year Follow-Up of a Study of Memory for the Attack of September 11, 2001: Flashbulb Memories and Memories for Flashbulb Events.” Journal of Experimental Psychology: General , vol. 144, no. 3, 2015, pp. 604-623. Web.

Ma, Wei Ji et al. “Changing Concepts of Working Memory.” Nature Neuroscience , vol. 17, no. 3, 2014, pp. 347-356. Web.

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Working Memory Model ( AQA A Level Psychology )

Revision note.

Working Memory Model

The working memory model.

•  The  Working Memory Model (WMM)  was proposed by   Baddeley and Hitch (1974)
• The WMM is a response to the over-simplification of short-term memory in the multi-store model

What are the components of the WMM?

Central executive

• This controls the WMM rather like the ‘boss’ in a business
• All information passes to the Central Executive (CE) which then decides which component should process it
• The components can only communicate with each other via the CE: they cannot ‘talk’ to each other
• The CE is modality free: this means it can process information from any of the 5 senses (auditory, visual etc.) 
• It can be inferred from research (Robbins et al. 1996) that the CE may be involved in highly complex tasks such as playing chess 

Phonological loop

• The phonological loop (PL) holds information in the form of speech/sound
• There are two parts to the PL: the phonological store/inner ear which deals with speech perception and the articulatory control process/ inner voice which processes speech production and rehearses verbal information
• There is more known about this component than any of the others as it is the easiest of the slave systems to test

Visuo-spatial sketchpad/scratchpad

• The visuospatial sketchpad (VSS) is concerned with visual and spatial information which it organises into separate components 
• The VSS also known as the inner eye
• There are two parts to the VSS: the inner scribe which deals with spatial information and the visual cache which stores information about form, shape and colour

Episodic buffer

• The episodic buffer (EB) was added to the WMM in 2000
• The EB is a temporary storage device used to integrate information from the VSS and PL
• The EB ensures that all the information from the slave systems links together and forms a cohesive whole which makes sense

Research support for the WMM

• Dual-task studies
• Baddeley and Hitch (1976) and  Robbins et al. (1996) : two tasks are possible at the same time if they use different slave systems e.g. the PL and the VSS: attempting two tasks using one slave system overloads that system
• The case study of brain-damaged patient  KF (Shallice & Warrington, 1970)

Evaluation of the WMM

• It extends on the work of the MSM and explains the complexity of STM with the tasks it can perform
• Research on dual tasks (Baddeley 1973) supports the idea of separate components and how they can be overloaded

Limitations:

• The WMM is vague on the link between STM and LTM
• It is difficult to measure the CE which means that not much is actually known about it (although this may well change as more research is conducted on it)
• If you draw the model in the exam it will help you to answer the question and may well earn you more marks
• Be clear and straightforward in your explanation of  how information is processed in the model

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Working Memory Model

Last updated 7 Nov 2023

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Baddeley and Hitch (1974) developed the Working Memory Model (WMM), which focuses specifically on the workings of short-term memory (STM).

Atkinson and Shiffrin’s Multi-Store Model of memory (MSM) was criticised for over-simplifying STM (as well as LTM) as a single storage system, so the WMM alternative proposed that STM is composed of three, limited capacity stores:

• Central Executive – this manages attention, and controls information from the two ‘slave stores’ [below]
• An articulatory rehearsal process (‘inner voice’) of language, including any language presented visually to convert to a phonological state, for storage in the:
• Phonological store (‘inner ear’), which holds auditory speech information and the order in which it was heard (or any visually-presented language converted by the articulatory process)
• Visuo-Spatial Sketchpad – this temporarily retains visual and spatial information

A later addition was the episodic buffer which facilitates communication between the central executive and long term memory.

The three-store STM stemmed from research using a ‘dual-task technique’ (or ‘interference tasks’), whereby performance is measured as participants perform two tasks simultaneously. The following observations provided evidence to suggest different, limited-capacity STM stores process different types of memory:

• If one store is utilised for both tasks, then task performance is poorer than when they are completed separately, due to the store’s limited capacity e.g. repeating “the the the” aloud and reading some text silently would use the articulatory-phonological loop for both tasks, slowing performance.
• If the tasks require different stores, performance would be unaffected when performing them simultaneously e.g. repeating “the the the” aloud whilst performing a reasoning task (requiring attention, i.e. the central executive), or whilst following a mobile stimulus with your eyes (using the visuo-spatial sketchpad).

Evaluation of the Working Memory Model

• The WMM provides an explanation for parallel processing (i.e. where processes involved in a cognitive task occur at once), unlike Atkinson and Shiffrin’s MSM.
• A Shallice and Warrington (1974) case study reported that brain-damaged patient KF could recall verbal but not visual information immediately after its presentation, which supports the WMM’s claim that separate short-term stores manage short-term phonological and visual memories.
• The model was developed based on evidence from laboratory experiments, so confounding variables could be carefully controlled to produce reliable results (that can be replicated).
• Despite providing more detail of STM than the multi-store model, the WMM has been criticized for being too simplistic and vague, e.g. it is unclear what the central executive is, or its exact role in attention.
• Results from laboratory experiments researching the WMM will often have low ecological validity (i.e. may not relate to real life), as tasks such as repeating ‘the the the’ are arguably not representative of our everyday activities.
• Working memory model
• Baddeley and Hitch (1974)

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Outline and Evaluate the Working Memory Model

Outline and Evaluate the MSM

The Multiple Store Model has three distinct stores; Sensory Memory-SM; this is things that are going on around you that you sense, Short Term Memory- STM; this is a store for items that you remember for a short period of time and finally Long Term Memory- LTM; this stores items for a long period of time.

Sensory Memory has a vast capacity of things that it can store; these can be things like noises outside, your temperature or hunger. The duration for this store is 50 milliseconds, this means that each item will only be stored for a very short period of time, but many can be stored. The way in which memory is stored in the SM is by touch, taste, visual, ecoustic etc. They way in which the memory transfers from SM  STM are by attention being given to the item. For example you will only realise that there are birds flying outside your window if your attention is being given to the things outside. This allows you to process and store the memory for longer. This leads on to Short Term Memory, which has a duration of 18 seconds in the STM. Encoding for STM is ecoustic and visual, which means it is stored by sound and images in the brain. Its capacity is 7±2 items, so either between 5-9 items. The transfer of STM  LTM is via rehearsal. This allows Short Term Memory items to be held for much longer period of time. LTM has an unlimited capacity and an unlimited duration. LTM is stored by semantic encoding.

In 1960 Sperling conducted an experiment and found evidence to indicate the Sensory Memory. Pps saw a table of letters in a blink of an eye (50 milliseconds), and then Sperling asked the Pps to write down the letters where they saw them. This shows that information decays rapidly in the Sensory Store.

This is a preview of the whole essay

Another example to support the separate memory stores is the research carried out by Peterson and Peterson into STM. They got Pps to look at “trigrams” (three letters), then they got them to could down in 3’s from a number and then asked them to recall the letters. 2% of Pps could recall after 18 seconds, this supports the STM.

The LTM was tested for by Shepard, he showed Pps 612 memorable pictures, then an hour later they were shown a few of these and some others and showed almost perfect recognition. Four months later they were still able to remember 50% of the pictures.

The primary and recency effects are when you remember the first and the last words of a list when recalled. The Primary effect comes from the LTM and the Recency effect comes from the STM.

Clive Wearing suffered from a bad case of the Herpes virus that damages his hippocampus that transfers memory form the STM to the LTM. His case provides the idea of a separate STM and LTM.

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18 Aug 2024  ·  Mengkang Hu , Tianxing Chen , Qiguang Chen , Yao Mu , Wenqi Shao , Ping Luo · Edit social preview

Large Language Model (LLM)-based agents exhibit significant potential across various domains, operating as interactive systems that process environmental observations to generate executable actions for target tasks. The effectiveness of these agents is significantly influenced by their memory mechanism, which records historical experiences as sequences of action-observation pairs. We categorize memory into two types: cross-trial memory, accumulated across multiple attempts, and in-trial memory (working memory), accumulated within a single attempt. While considerable research has optimized performance through cross-trial memory, the enhancement of agent performance through improved working memory utilization remains underexplored. Instead, existing approaches often involve directly inputting entire historical action-observation pairs into LLMs, leading to redundancy in long-horizon tasks. Inspired by human problem-solving strategies, this paper introduces HiAgent, a framework that leverages subgoals as memory chunks to manage the working memory of LLM-based agents hierarchically. Specifically, HiAgent prompts LLMs to formulate subgoals before generating executable actions and enables LLMs to decide proactively to replace previous subgoals with summarized observations, retaining only the action-observation pairs relevant to the current subgoal. Experimental results across five long-horizon tasks demonstrate that HiAgent achieves a twofold increase in success rate and reduces the average number of steps required by 3.8. Additionally, our analysis shows that HiAgent consistently improves performance across various steps, highlighting its robustness and generalizability. Project Page: https://github.com/HiAgent2024/HiAgent .

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