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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Theories of Working Memory: Differences in Definition, Degree of Modularity, Role of Attention, and Purpose

Eryn j. adams.

a Department of Psychological Sciences, University of Missouri, Columbia

Anh T. Nguyen

Nelson cowan.

The purpose of this article is to review and discuss theories of working memory with special attention to their relevance to language processing.

We begin with an overview of the concept of working memory itself and review some of the major theories. Then, we show how theories of working memory can be organized according to their stances on 3 major issues that distinguish them: modularity (on a continuum from domain-general to very modular), attention (on a continuum from automatic to completely attention demanding), and purpose (on a continuum from idiographic, or concerned with individual differences, to nomothetic, or concerned with group norms). We examine recent research that has a bearing on these distinctions.

Our review shows important differences between working memory theories that can be described according to positions on the 3 continua just noted.

Once properly understood, working memory theories, methods, and data can serve as quite useful tools for language research.

Working memory can be described as a limited amount of information that can be temporarily maintained in an accessible state, making it useful for many cognitive tasks. It is one of the most influential topics discussed in psychological science. One of the reasons for its popularity is the vast variety of activities and cognitive processes in which working memory is thought to play a role. As a real-world type of example, suppose a teacher tells the class that Earth is the third planet from the sun and asks a particular student to find it on a map of the solar system posted on a wall. The child must remember the first part of the teacher's speech (about the Earth's location) while processing the second part (the request for the child to find it on the map; cf. A. Baddeley, 2003 ). At this point, thoughts about performing in front of the class and how to handle that social demand may preoccupy working memory, competing with the assigned task. The point that Earth is the third planet must be retained in a ready form while the child implements a potentially tricky routine of counting, starting not with the sun itself but with the planet closest to it. The child also has to remember to stop counting at the correct planet when the number 3 is reached and then, perhaps, look toward the teacher for feedback. The limits of working memory are such that there are many points at which this hybrid process can go awry because multiple skills compete for a limited working memory capacity. In a different kind of example, a young child can understand what is meant by a tiger only by holding in mind and combining three features, big , cat , and striped ; a tiger is a big cat with stripes. These features distinguish a tiger from, in turn, a house cat (not big), a zebra (not a cat), and a lion (not striped; cf. Halford, Cowan, & Andrews, 2007 ).

Although examples like the ones presented above give us an idea of how working memory functions, it is often difficult to find one definition that encompasses all applications of working memory. Often, different theories—of working memory or otherwise—cannot be compared directly because the theories, though nominally on the same topic, actually are based on subtly different definitions of what is being studied. Cowan (2017a) examined the definitions of working memory commonly stated or implied in the research literature and listed nine definitions. Here, we cover only a definition that should apply to all of the theories of interest and, then, more specific definitions tied to the major theories that will be described in detail.

In a definition that seems most generic and usable across different theories ( Cowan, 2017a ), working memory is a system of components that holds a limited amount of information temporarily in a heightened state of availability for use in ongoing processing. The definition does not depend on statements about the exact organization of components that may store or process information. This definition allows us to think of working memory information as separate from the rest of memory and uniquely important in carrying out cognitive tasks, and we believe that the field as a whole would not strongly object to this working definition.

To our knowledge, the earliest mention of the term working memory originated not from the study of the human brain but from the study of the computer. Computer scientists utilized the term working memory to refer to structures they set up within their programs to hold information that was needed only temporarily in executing procedures, such as solving geometry proofs ( Newell & Simon, 1956 ). Although humans are unable to manage multiple temporary storage structures at once like computers, still, it is instructive to realize that the need for temporary storage arose in the process of inventing problem-solving routines. The use of the term working memory for human research started with Miller, Galanter, and Pribram (1960) . They considered working memory as a part of the mind that allows us to operate successfully in life, completing our goals and subgoals by storing the useful information needed to execute these planned actions. For example, the goal of furthering one's career can have a subgoal of getting an academic degree, with a sub-subgoal of making it to class today, a sub-sub-subgoal of getting dressed, and so on, down to one's momentary activities. Forgetting information at the wrong time leads to errors.

A. D. Baddeley and Hitch (1974) jump-started the field of working memory, and they defined the state of affairs preceding their paper as the short-term or immediate memory view on the basis of what they called the modal model or very usual type of model at the time. The most-often-cited example was the work of Atkinson and Shiffrin (1968) . In that work, short-term memory was represented by a single mechanism that temporarily held information to be used in processing. The most common task leading to that conception was a simple span task in which, on each trial, a list of verbal items was presented and was to be repeated back verbatim; the longest list that could be repeated correctly is the memory span. Atkinson and Shiffrin focused also on control processes used to shuttle information between stores, as when knowledge is used to enrich the contents of the short-term store.

In the research-rich book chapter of A. D. Baddeley and Hitch (1974) , the term working memory came to them as they attempted to distinguish their views from the modal model. Their definition of working memory was as a multicomponent system to store temporarily information as it is processed. Baddeley and Hitch found results that they could not represent by a single process, as if they had to break the box representation into multiple boxes, which they called multiple components of a system they termed working memory . One component held verbal information (the phonological store), another component held visual and spatial information (the visuospatial store), and yet another component was a processor (the central executive), responsible for moving information into the stores and using them to guide behavior. In the most recent version of A. Baddeley's (2000) model, another component (the episodic buffer) temporarily holds semantic information and associations between different kinds of information (e.g., face-to-name links).

In contrast to simple span tasks, the tasks that A. D. Baddeley and Hitch (1974) presented typically involved retaining a list in memory while carrying out another process, like completing a reasoning problem, and then recalling the list. When multiple stimuli have to be processed, there is supposed to be interference between stimuli that are being retained or processed using the same kinds of information codes, such as two verbal tasks or two spatial tasks, but not interference between information held in different codes, such as a verbal list to be recalled and a concurrent spatial task. Interference is supposed to occur only when working memory representations of two or more stimuli depend on the same component or store at the same time.

Many researchers interested in the application of working memory to real-world types of cognitive function, including language processing (e.g., Daneman & Carpenter, 1980 ; M. A. Just, Carpenter, & Woolley, 1982 ), have adopted a slightly different emphasis on the basis of the work of A. D. Baddeley and Hitch (1974) and follow-up work (e.g., A. Baddeley, 2000 ). They distinguish between the situation when one only has to store and then repeat information without processing or manipulating it, which they call short-term storage , and the situation in which one has to manipulate the stored information, which they term working memory . For example, if you hear a list of grocery items and just have to repeat the list, that would be termed a test of short-term memory , whereas if you hear a list of grocery items and have to repeat them in a different order, with vegetables and fruits first, dairy items second, and other items third, that would be termed a test of working memory (though others use the terms slightly differently; see Cowan, 2017a ). These researchers were not so concerned about whether this working memory was a multicomponent system or not.

Organization

We will next discuss some ways in which working memory is important for language. Then, we will present three often-discussed theories that illustrate different ways in which working memory can be conceived (the already-mentioned theories of Atkinson & Shiffrin, 1968 , and A. Baddeley, 2000 , and a different conception by Cowan, 1988 ). Finally, we will discuss working memory theories within an organizing framework in which we point out three dimensions on which the theories differ, namely, (a) the degree of modularity, (b) the degree of reliance on attention, and (c) the purpose of the theory as elucidating individual differences versus group means. These dimensions will be presented as continua on which different theories can be placed. In a final section, recent research on working memory pertaining to these dimensions will be highlighted. The evidence suggests a fortunate convergence of the different theories in recent years, and implications for future language research are discussed.

The Importance of Working Memory in Language Processing

The other articles in this issue of the journal provide a detailed picture of the use of working memory in language, so here, we simply give an initial glimpse of this use to illustrate the relevance of our descriptions of models of working memory. In academically relevant areas, including problem solving, learning, reasoning, and mathematics (numerical, symbolic, and spatial), among other areas, working memory capability has often turned out to be one of the best predictors of cognitive performance. For our purposes, we will briefly discuss how working memory is important to language comprehension and production.

Although materials of all sorts can be held in working memory, it has long been noticed that different materials are not on equal footing. Conrad (1964) found that even when letters were presented in visual form to be remembered, mistakes consisted primarily of acoustic rather than visual confusions, suggesting that participants were in some way converting visual materials to a phonological (speech-based) code. Subsequent work on the effects of manipulations to encourage or discourage the use of speech codes suggested that the special privilege of verbal materials is that they can be covertly or overtly repeated, or rehearsed, without much effort to keep the working memory active. This kind of concept about the role of language was represented in the A. D. Baddeley and Hitch (1974) theory of working memory. The relation to language was amplified when it was determined that the ability to remember and repeat phonological sequences, such as multisyllabic nonsense words or short series of words, was critically important for vocabulary learning (e.g., A. D. Baddeley, Gathercole, & Papagno, 1998 ).

The focus of A. D. Baddeley and Hitch (1974) on phonological processes and rehearsal was important in order to make intensive progress in understanding one part of the working memory system and how it actually operates. Other researchers were interested in working memory and language on a more holistic level in order to determine how working memory functions for a common task, such as reading. Daneman and Carpenter (1980) and Case, Kurlund, and Goldberg (1982) , therefore, devised working memory span tasks in which multiple components are presumably involved. In a reading span task, Daneman and Carpenter presented lists of sentences for which the participant had to do a comprehension task (engaging processing) while also remembering the final word of each sentence (engaging storage). After the last sentence, the list of sentence-final words was to be recalled. Performance was assessed as the number of sentences that could be processed correctly while still permitting correct recall of the final words of the sentences. Case et al. similarly devised a counting span task in which series of arrays of simple objects were to be counted and the sum of each array was to be retained in memory and, then, recalled after the last array was counted. These complex span tasks correlated much better than simple digit span with verbal abilities, including reading ( Daneman & Carpenter, 1980 ), though it was later observed that complex span tasks also correlate well with aptitudes across domains, not just language aptitudes (e.g., Cowan et al., 2005 ; Kane et al., 2004 ).

A great deal of the research on the implications of working memory on language processes originates from research on language disorders (e.g., de Jong, 1998 ; Gathercole & Alloway, 2006 ; Swanson, 1999 ). Gathercole and Baddeley (1990) studied children with developmental language disorders compared with control groups on multiple working memory–related tasks. Their results showed that children with language disorders performed at lower levels than age-matched peers on nonword repetition tasks and, sometimes, even lower than younger peers matched on vocabulary and reading. Another experiment in the study showed that children with language disorders did not differ from peers on their ability to rehearse information. These and other supporting findings suggested that children with language disorders do have working memory storage deficits, which could contribute to, or perhaps even cause, the disorders. Subsequent research goes further to try to understand the mechanisms of working memory deficits and language disorders, including specific language impairment (see Marton & Schwartz, 2003 ; Montgomery, 2003 ; Weismer, Evans, & Hesketh, 1999 ). Other research shows how working memory deficits in retention of serial order information are involved in language impairment ( Gillam, Cowan, & Day, 1995 ) and dyslexia ( Cowan et al., 2017 ; Majerus & Cowan, 2016 ).

One growing line of research deals with the implications of working memory in second language acquisition and use. In a world where many individuals are exposed to and juggle more than one distinct language, understanding the processes that underlie successful processing is of utmost importance. Working memory is thought to be a critical ability in the acquisition of a second language, though the mechanisms remain unclear ( Cowan, 2015 ). In an example of expert language use, cross-language interpreters face the task of trying to hold the information spoken by the original speaker and what they have already translated, as well as the gist or the topic of the conversation at hand ( Cowan, 2000/2001 ). Their work requires intensive attentional filtering and attention switching, as well as temporary storage, or working memory capacity.

Although there is much supporting evidence for the importance of working memory in language processing, the exact role of such a source has been debated in several ways in the last few decades. One such line of debate concerns the role of working memory in syntactic processing. M. Just and Carpenter (1992) proposed a theory in which language comprehension is constrained by working memory capacity. Included in this theory was a proposal that the modularity of language processing is best explained as a capacity constraint rather than one of architecture. Thus, individuals with smaller working memory capacities may not have enough available activation to process and store nonsyntactic information during syntactic processing. Individuals with larger working memory capacities should then be able to handle both syntactic and nonsyntactic information at once and may experience an influence of the nonsyntactic information on syntactic comprehension. These differences might cause some people to appear to have more modular language processing than others, but the authors proposed that it all depends on their working memory capacity for language, not a distinct separation of modules.

M. Just and Carpenter (1992) called upon a previous study ( Ferreira & Clifton, 1986 ) in which readers processed garden-path sentences with or without semantic information that could steer the interpretation of syntax. In the sentence, “The defendant examined by the lawyer turned out to be unreliable,” it is at first possible to think that the defendant is the one doing the examining, the garden-path interpretation that leads participants to spend a long time looking at the word by , presumably because their initial interpretation was wrong. In the sentence, “The evidence examined by the lawyer turned out to be unreliable,” in contrast, the nonanimacy of the subject “evidence” should be a clue that the agent who does the examining comes later in the sentence; yet, participants still dwelled on the word by , showing that they were captured by the garden-path interpretation even though it is semantically implausible. Just and Carpenter replicated the study, this time separating individuals according to their span. Low-span individuals were still led down the garden path, as previously found, whereas high-span individuals were able to take into account the nonsyntactic information. The authors concluded that syntactic processing in high-span individuals was not modular but interactive, suggesting a domain-general capacity that applied to both syntax and nonsyntactic contextual information. Recent evidence also suggests that high-span adults are more likely to keep their options open longer when trying to resolve the meaning of ambiguous printed sentences; lower span adults tend to break up the text up into smaller chunks and seize upon convenient interpretations on the basis of the chunks without waiting for more input ( Swets, Desmet, Hambrick, & Ferreira, 2007 ).

In a critique of some aspects of the capacity-based theory, Waters and Caplan (1996) proposed that Just and Carpenter's interpretation of the garden-path results was not adequate. They noted that their method was not actually a direct replication of the original methods utilized by Ferreira and Clifton and, therefore, could not be interpreted in the same ways. Also, they pointed out that the data reported by Just and Carpenter still showed that individuals with both low and high spans experienced the garden-path error for some sentences. Waters and Caplan suggested that these trends in the data only further confirm the modularity view of syntactic processing. The authors also argued that, if the Just and Carpenter theory is correct, language processing results should show differences in overall sentence processing that are related to working memory capacity. They note that this difference was not always found in some previous studies and that, in one study, low-span individuals were able to use pragmatic information to help assess sentence meaning but high-span individuals were not ( King & Just, 1991 ).

Though Just and Carpenter disagree with Caplan and Waters on the modularity of language processing and on the role of working memory during this processing, one aspect of their theories that they share is the proposal that linguistic knowledge and working memory are two separate entities. Carpenter, Miyake, and Just (1994) offered evidence from readers with brain injury or disease in which the lexicon and production rules remained intact but storage and processing of language were severely impaired. They proposed that these results supported the idea that what one knows about language (i.e., language competence) and how language is processed (i.e., language performance) are two different entities. However, MacDonald and Christiansen (2002) proposed a resolution in which knowledge and capacity actually cannot be considered separately because the processing and storage stems from a passing of activation through a common learning network.

In sum, though some of the exact mechanisms of the involvement of working memory in language processing have been debated and are uncertain, there is plenty of evidence supporting a conjoining of the two fields of study. Working memory is an important cognitive skill to consider when approaching the study of individual differences in language processing, comprehension, and production, as well as language development and disorders.

Three Examples of Working Memory Theories

To explore in greater detail some theories of working memory, Figure 1 illustrates three often-mentioned theories. The top panel shows a schematic depiction of what Alan Baddeley has often light heartedly called the modal model , meaning the type of model of which the most instances existed (circa the late 1960s). The best-known example is that of Atkinson and Shiffrin (1968) , though a precursor is found in a footnote of a book by Broadbent (1958) . A large amount of incoming sensory information is mostly forgotten, but a small amount of the information advances to a working memory, where it is enhanced by long-term memory information and temporarily retained. Working memory is also the basis of the formation of new long-term memories. As evidence of the need for separate short-term and long-term mechanisms, Atkinson and Shiffrin stressed the effects of hippocampal lesions, which show diminished long-term memory storage with preserved short-term storage (e.g., Milner, 1968 ). Their model also placed an emphasis on control processes (not shown), which strategically help to recirculate information in working memory and shuttle information between working memory and long-term memory.

A depiction of three models of working memory. Top: what Baddeley has termed the modal model , for example, after Atkinson and Shiffrin (1968) ; middle: a version of the A. D. Baddeley and Hitch (1974) model or a recent revision of it by A. Baddeley (2000) ; bottom: embedded-processes model of Cowan (1988, 1999) .

The middle panel of Figure 1 shows the model that sparked the field of working memory, initiated on the basis of a large number of experiments ( A. D. Baddeley & Hitch, 1974 ) and then put through several iterations ( A. D. Baddeley, 1986 ; A. Baddeley, 2000 ). The key difference between this and the modal model is that working memory here has been split into a few different specialized stores and a more general store. One specialized store (left-hand box in the middle panel) is for phonological information, and another (right-hand box in the middle panel) is for visuospatial information. The more general store (shown between the phonological and visuospatial stores), called the episodic buffer , is not specialized for any one kind of information but available to link different kinds and is possibly tied to attention. Long-term memory feeds category information into the stores used to guide the interpretation of sensory input. Similar to the modal model, Baddeley's model includes some set of mechanisms, collectively called the central executive , that govern the strategic control of information. This component may be even more sophisticated than the control processes of the modal model because, in Baddeley's model, there are more separate stores to contend with and, therefore, more potential mnemonic strategies and manners of processing information. Among its other activities to schedule and prioritize information transfers and behaviors, the central executive initiates a rehearsal process to prevent decay of information from the stores.

The bottom panel of the figure depicts the embedded-processes model proposed by Cowan (1988) , named by Cowan (1999) , and enhanced with a clearer notion of its central, capacity-limited portion by Cowan (2001) . Unlike the Baddeley model, which was focused around the kinds of effects he and his colleagues were observing in the laboratory, Cowan's model was an attempt to establish a more general framework for information processing insofar as it was known. Information comes in from the environment through a very brief sensory store (as depicted by rightward-pointing arrows), activating features in long-term memory corresponding to the sensory properties of the incoming information and its coding: phonological, orthographic, visual, and other simple features from the senses. The phonological and visuospatial stores are not separated in this model because it is assumed that there is a rather complex taxonomy and that it is uncertain which stores are basic, which are overlapping, and so on. In place of showing separate stores, the same evidence is accommodated by the simple proposal that new input overwrites or interferes with previous activated information with similar features. As in Baddeley's model, the information supposedly decays if not rehearsed or, alternatively, is more quickly and nonphonologically refreshed via attention ( Barrouillet, Bernardin, & Camos, 2004 ; Cowan, 1992 ; Raye, Johnson, Mitchell, Greene, & Johnson, 2007 ).

Some kind of filtering function that limits how much information gets into working memory seems necessary in any model of processing (cf. Broadbent, 1958 ). Cowan (1988) suggested a specific mechanism for it, dishabituation of orienting. In the orienting response, an individual's attention is turned to a stimulus that stands out from the background in the environment. It may be a sudden change in the environment or a newly presented item of special meaning to the individual. With repetition, the novelty soon wears off, and the orienting response dies down or habituates. In such a mechanism, all information from the environment stimulates physical features, but a neural model of the environment is built up over time, and only the information discrepant with the model causes dishabituation or a restrengthening of a previously weakened response and, thus, attracts the focus of attention. That focus also can be directed by the central executive, which allows it to pick up more abstract, semantic information voluntarily. The focus of attention allows a coherent organization and interpretation of the information it contains, but that information is limited to a few separate, known items at a time. The separate items can be linked to form a new memory that becomes part of the long-term record. When items leave the focus of attention, they still remain activated for a while. These previously attended, meaningful items, along with the never-attended physical features of the rest of the environment, all contribute to the neural model, and any noticed change from the neural model attracts attention. The changes can be physical, often regardless of attention, or semantic, usually with attention. Thus, the activated features from long-term memory, including any newly formed memories, along with the current focus of attention, together comprise the working memory system. This system is limited by interference and decay of activated memory and by a capacity limit of the focus of attention. Fatigue of the focus of attention also is possible.

Theories of Working Memory Distinguished on Several Continua

In the next part of this review, we will differentiate some well-known and representative theories of working memory, beyond those we have discussed in detail, by focusing on three main continua that tend to differentiate them: the degree of modularity, the role of attention, and the nomothetic versus idiographic purpose. Though these continua are not the sole discriminating issues, they provide a useful orientation for understanding differences among working memory theories. We will name theories that lie on either extreme of each continuum and also theories that tend to straddle the middle, at least as we perceive them. Other theories, in addition to the ones previously described, will be mentioned briefly within the continua to assist further exploration. We will also highlight language, speech, or auditory research that supports or rebuts relevant theories. Figure 2 illustrates the continua and how we have rated various theories on them.

Three continua discriminating models of working memory from one another. Top, the degree of modularity; middle, the degree to which attention underlies the storage and handling of stored information in the model; bottom, the dependence and use of idiographic (individual difference) versus nomothetic (group mean) information.

Degree of Modularity

Modularity deals with the organization of the system of working memory and how compartmentalized it is. If working memory were a house, a highly modular theory would be a house with many rooms, or modules, each designated to a specific type of information. A less modular theory would have fewer, bigger rooms that process and store all types of information. Thus, modules of working memory are functioning parts of the system that store, maintain, or process different types of information independent of one another. Information can be categorized on the basis of different types of characteristics. Some theories that could be considered modular (to a degree) separate stores on the basis of the amount of time the information has been held (short term vs. long term). Other, more modular theories may take time into account but also separate stores on the basis of the type of information (verbal, visuospatial, etc.). The modules, however, are not necessarily separate brain areas and could overlap neurally. By analogy, the U.S. government in Washington, D.C., includes three branches (modules), but any one geographical area in Washington can include representations of two or even all three branches.

Certain consequences arise from regarding working memory as either modular or not. In a modular theory, if one module is at capacity in terms of the amount of information it can actively store or process, other modules are still available for use. Less modular theories imply instead that, when these nondiscriminatory areas of working memory are at capacity, no type of information beyond capacity will be processed or stored successfully. In what follows, we examine a theory with no modularity and, then, consider different types and degrees of modularity.

Unitary Theories With No Working Memory/Long-Term Memory Distinction

If working memory is to differ from long-term memory, we can think of two basic ways in which this difference can occur. There must be information in working memory that is limited to a certain time period, a temporal decay property, or limited to a certain amount of information, an item capacity property. Either of these properties could be modulated by the amount of interference. Nevertheless, if they do not exist at all, there would be only one kind of memory as posited by unitary memory theories, which forgo any separation of short-term or working memory versus long-term memory. (We will see that some such theorists still exist.) One of the earliest researchers to propose such a view was McGeoch (1932) , who sought to argue against Thorndike's (1914) proposed law of disuse. Thorndike suggested that, when a stimulus–response association is not activated for a long time, the strength of the connection decreases. One might then distinguish between short-term, labile memories versus longer-term memories that remain because of repeated use. McGeoch argued, however, that disuse does not always mean forgetting. For example, he referred to a study showing recovery of conditioned responses during a period of inactivity following experimental extinction. If memories do not always grow weaker over time, the argument goes, there is no reason to talk of a short-term memory separate from long term, an argument that was reinforced by Underwood (1957) . He proposed that most forgetting came from some combination of interference that was proactive (from previous stimuli in the experiment or in everyday life) and retroactive (from information received between the stimulus and test), both of which could impede fully accurate memory of target items. According to this view, the recency of a memory does not directly distinguish it from older memories; only the amount of interference that has occurred does.

Against unitary theory, Peterson and Peterson (1959) carried out a study in which letter trigrams were presented, and before they were to be recalled, a variable period of counting backward by 3 was introduced to prevent rehearsal. The researchers found that letter memory declined dramatically as the period of counting backward increased from very short to 18 s, despite the dissimilarity of letters to numbers. This decline was taken as an indication that a short-term memory of the letters decayed over time. Keppel and Underwood (1962) , however, showed that, in this kind of procedure, the dramatic drop-off did not occur at all on every participant's first trial but developed over trials. They suggested that proactive interference from previous trials increases as the retention interval on the present trial increases, removing the temporal context of the most recent items. Keppel and Underwood's interpretation from the unitary memory view was that proactive interference alone accounts for the effect of the retention interval. An alternative, two-store interpretation that Keppel and Underwood did not consider is that there could be a short-term memory that decays over 18 s and also a long-term memory of the present memoranda that can be used, at all retention intervals, on the first few trials. Proactive interference builds up across trials quickly, and after it has built up, long-term memory no longer contributes much to recall; this change can explain why forgetting over retention intervals appears in later trials, as the participant becomes more dependent on a temporary short-term memory.

In another example of evidence seemingly favoring unitary memory theory, Bjork and Whitten (1974) challenged the notion that, in free recall of a verbal list, a pronounced advantage for recall of the most recently presented items (recency effect) is the evidence that those items are recalled from short-term memory. Glanzer and Cunitz (1966) had shown that requiring a distracting task of counting aloud for 30 s before written recall abolishes the recency effect, and they attributed that effect to the degradation of a short-term store. Bjork and Whitten, however, reinterpreted the recency effect in terms of the temporal distinctiveness of the end of the list or how separate in time from one another items on a list seem. Better temporal distinctiveness is supposed to facilitate the task of retrieving the right information from memory. As the distraction period continues, loss of that distinctiveness occurs and, thus, increases proactive interference. By separating all list items by distracting tasks, they were able to preserve a recency effect despite a distraction-filled period after the list and before recall, presumably because that final period could no longer reduce temporal distinctiveness very much in these spaced lists.

Most current theorists acknowledge that there is sometimes a contribution of temporal distinctiveness and proactive interference, as the unitary theorists assume. However, they also point to evidence that a recency effect obtained with distractors between items has properties different from a recency effect obtained even when temporal distinctiveness is low, evidence for a separate short-term store after all (see discussion of the “monistic view” by Cowan, 1995 ; and see Davelaar, Goshen-Gottstein, Ashkenazi, Haarman, & Usher, 2005 ).

Absence of decay in unitary theories . One of the main issues that separates unitary theories of memory from theories that are more modular is that proponents of unitary memory theories do not believe that memory decays over time. Nairne (2002) suggested that certain memory cues (e.g., how pronounceable or tangible to-be-remembered items are) affect short-term retention just like they do long-term retention and that rehearsal and decay prove inadequate to explain forgetting. The original evidence for decay under cross-examination by Nairne was that individuals can recall lists of about as many items as they can repeat in about 2 s ( A. D. Baddeley, Thomson, & Buchanan, 1975 ). The speed of repetition was assumed to approximate the speed of covert rehearsal and could be manipulated both by presenting words that took less or more time to say and by correlating performance with individual differences in the repetition rate. Nairne, however, pointed to a study by Schweickert, Guentert, and Hersberger (1990) showing that, when participants were presented with lists of similar and dissimilar words at the same pronunciation rate, there were still span differences between the two types, suggesting that time alone is not a sufficient account of forgetting. In general, Nairne argued that, although time is correlated with forgetting, it is the events that happen during a particular time period that are important for the loss of memory, not the passage of time. Therefore, he suggested that theorists should move on to a model of memory that recognizes short-term retention as largely cue driven. Evidence for cue-driven accounts of short-term retention includes characteristics of stimuli, such as lexicality, word frequency, or concreteness resulting in differences in recall. An even stronger statement against decay has been made ( Neath & Brown, 2012 ) to the effect that only distinctiveness, interference, and retrieval context make a difference. Jalbert, Neath, and Surprenant (2011) found that, when short and long words were matched for neighborhood size (the number of words similar to the target word in linguistic features), the word length effect was eliminated. Oberauer and Lewandowsky (2008) showed, against the expectation on the basis of decay, that the passage of time during recall made little difference, even with suppression of rehearsal and another, nonverbal task to engage attention.

Theories Distinguishing Working Memory From Long-Term Memory Based on Decay of Items From Working Memory

A. D. Baddeley et al. (1975) and A. D. Baddeley (1986) invoked decay and rehearsal to explain why participants could recall lists of as many items as they could read aloud or recite from a memorized series in about 2 s. Presumably, the memory trace of the entire list had to be rehearsed in that amount of time or some of the items would be lost through decay. Barrouillet et al. (2004) proposed the same theory except that, in place of rehearsal, they proposed that refreshing through attention could be used. The evidence consisted of a negative, linear relation between the memory span and the proportion of time between items that was occupied by a distracting task, termed the cognitive load . The notion is that the cognitive load prevents refreshing and, therefore, allows items to decay. More recent work has suggested that either rehearsal, within the verbal domain, or attention-based refreshing, regardless of the type of materials, might be used together in various combinations ( Camos, Mora, & Oberauer, 2011 ; Vergauwe, Barrouillet, & Camos, 2010 ).

These theories assume decay in the absence of rehearsal and refreshing and rely on that assumption but generally have not observed decay directly. Ricker, Spiegel, and Cowan (2014) did find that arrays of unfamiliar characters were forgotten over a number of seconds as a function of the retention interval between the array and a recognition probe; this trend was observed even when a temporal distinctiveness account could be ruled out. Subsequent work suggested that decay occurred only when there was not sufficient time after the presentation of stimuli for them to be well encoded into working memory in the first place (working memory consolidation: Ricker, 2015 ; Ricker & Cowan, 2014 ; Ricker & Hardman, 2017 ).

Although the decay observed by Ricker and colleagues allows one version of the embedded-processes model of Cowan (1988 , 1999) , it is problematic for some other theories. Ricker et al. actually have shown very little decay in situations similar to those in which decay has been used to help account for the 2-s rehearsal limit for lists that can be recalled ( A. D. Baddeley et al., 1975 ) and for the cognitive load function ( Barrouillet et al., 2004 ).

Working Memory Distinguished From Long-Term Memory by Working Memory Capacity Limits

In place of decay, there could be an interference-based loss rate that depends on the amount of information held concurrently ( Davelaar et al., 2005 ; Melton, 1963 ), that is, functionally, some sort of capacity limit. For example, in a Peterson and Peterson–type procedure (lists to be recalled after counting backward), the rate of forgetting is steeper when there are more letters in the set to be remembered, presumably because of within-set interference ( Melton, 1963 ; cf. Murdock, 1961 ).

According to Cowan's (1988 , 1999) embedded-processes model of working memory, the focus of attention is quite limited in capacity. Cowan (2001) explored what the average individual's memory span is when stimuli are presented in a way that prevents mnemonic strategies like rehearsal, chunking, and grouping. Chunking is the process of using what one already knows to make larger collections of items, reducing the amount to be remembered; an example is remembering the list IRSCIAFBI more easily as three acronyms for government agencies (the Internal Revenue Service, the Central Intelligence Agency, and the Federal Bureau of Investigation). Grouping refers to the process of combining items to form new collections that may be rapidly memorized. For example, one may memorize a list of nine digits by mentally separating the digits into groups of three (e.g., 674, 891, 532). When strategies such as these are prohibited, typical span for various kinds of materials (both verbal and nonverbal) is reduced from Miller's (1956) 7 ± 2 to about 4 ± 1, on average ( Cowan, 2001 ). The limit seems to hold for a wide variety of item types, though sometimes the observed capacity is lower because memory of complex items does not capture all of the details of the items ( Awh, Barton, & Vogel, 2007 ).

There has been a challenge from theorists who believe that the observed capacity is actually a fluid resource that can be spread thinly over all items in an array or series (e.g., Ma, Husain, & Bays, 2014 ). However, recent work suggests that, if this is the case, after about three items, the fluid resource must become so thin as to be of no use ( Adam, Vogel, & Awh, 2017 ), essentially removing empirical differences between the finite-slots and fluid-resources theories of working memory capacity limits.

The Modularity Continuum

The top panel of Figure 2 shows a continuum of some models of working memory arranged from less modular on the left to highly modular on the right. The unitary theory is of course considered nonmodular. The embedded-processes model is just slightly more modular because its two mechanisms for working memory are nested rather than separate, with both of them nested within the long-term memory system. The modal model has separate short-term and long-term stores but still no proposed, specific structure within short-term (i.e., working) memory. The multicomponent model is yet more modular, with separate stores for different types of code (verbal–phonological, visual–spatial, and sematic–binding).

We also include a couple more models in Figure 2 that are not discussed in detail. A model by Schneider and Detweiler (1987) actually goes in an even more modular direction, suggesting, at a microscopic scale, separate modules for auditory, speech, lexical, semantic, motor, mood, context, and visual stimuli, all under higher levels of control. In perhaps the most modular approach, Logie (2016) proposes that there are not only modules for specific kinds of materials, as in the multicomponent model, but also modular mechanisms replacing the central executive (cf. Vandierendonck, 2016 ).

Finally, note that, in the field of language, there similarly have been lively debates about whether language is represented in the brain in a very modular way (in which syntax is insulated from other aspects of language processing) or in a less modular way (in which syntax is one outcome of a general process limited by working memory constraints). It is possible that the more (or less) modular language theories naturally line up with the corresponding more (or less) modular working memory theories, and considering the nature of working memory and language modules together might shed light on the general nature of cognition, as well as yielding practical insights into the best ways to teach language and remediate language disorders.

Role of Attention

It is generally, though not uniformly, the case that less modular theories of working memory have a higher reliance on attention. The main reason is that attention is conceived as the storage device that is limited but that can seize upon any kind of information, retaining, for example, some verbal items, some visual images (which may be related to the verbal information, as in a television commercial) and, even, some touches, musical sounds, and other sensations that have been meaningfully interpreted. Any such general storage across domains is capacity limited in that, although people perceive the entire scene (e.g., an arrangement of objects that looks like a kitchen), there is inattentional blindness for the exact properties of all but a few attended aspects of the scene. If the scene flickers or attention is drawn to a certain aspect of the scene, it is possible to replace one object with another, such as substituting a coffee maker with a toaster or with nothing at all, and observers tend not to notice except in rare instances in which attention was already focused on the changing object (e.g., Simons, 2000 ).

If working memory is limited by how much material is included in the focus of attention at once ( Cowan et al., 2005 ), there are important implications for language processing. The easiest way to process language, much like processing visual materials, is to fit the received language input into a comfortable scheme that seems right without necessarily attending to all of the details. Results of Patson, Darowski, Moon, and Ferreira (2009) suggest that this is often the case. Adults who read a sentence like “While Janice dressed the baby slept” often came away with an impossible interpretation of that sentence (in this case, that Janice dressed the baby while the baby slept). Inattentional blindness to the part of the syntax would seem like a case for an attention-based working memory store that is indeed involved in ordinary language processing, regardless of language competence.

The Attention Continuum

The middle panel of Figure 2 shows a continuum on the basis of the degree of usage of attention by working memory, from low (on the left) to high (on the right). Logie's (2016) formulation seems not to subscribe to the notion of attention at all. Oberauer and Lin (2017) follow Oberauer's earlier work by subscribing to a single-item focus of attention in most situations, though the attention focus is capable of expansion when, say, two items need to be considered together. The multicomponent model makes rather more use of attention at least for processing, in the form of the central executive and its choices. The extent to which storage also relies on attention is a question currently in flux within that approach. In the embedded-processes model, attention is used not only for processing but also clearly for storage. Engle (2002) and Barrouillet et al. (2004) are similar in that one attention process seems critical for performance (correct goal maintenance in the face of interference and distraction, e.g., Kane & Engle, 2000 ; or refreshing of items before they decay). Finally, James (1890) discussed a mechanism that was nothing but the attention focus: primary memory that was essentially the same as the information in consciousness, most comparable to the focus of attention component of Cowan's model.

In the discussions of language disorders, there have been considerable debates about the degree to which the disorders stem from automatic components of processing versus those that depend on attention and central executive function. Keeping in mind the alternative models of working memory that differ on the role of attention should help to inform this debate.

Nomothetic Versus Idiographic Purpose

It is natural that some researchers are most interested in using working memory models to understand individual differences, known as idiographic information , whereas others are interested to understand how humans process information in general, known as nomothetic information . What might be less well-appreciated is how these approaches can actually work together. For example, if one wanted to distinguish between different modules or mechanisms, nomothetic researchers could hope to do so by showing dissociations within an individual (such as the findings of A. D. Baddeley & Hitch, 1974 , indicating that a separate memory load did not reduce the recency effect in free recall, or that two sets of phonological materials interfere with one another more than one phonological set and one visual, nonverbal set). Sometimes, however, idiographic information is used for a similar purpose of model description, under the assumption that tests that assess a particular mechanism within the working memory system (e.g., the phonological loop) will yield individual differences that do not completely correspond to the individual differences observed in tests of a different mechanism (e.g., the visuospatial sketchpad). It was from this perspective that Gathercole, Pickering, Ambridge, and Wearing (2004) used structural equation modeling to show that children from 4 years up showed a working memory structure similar to the multicomponent model. In structural equation modeling, groups of correlated variables with a common purpose are taken as alternative measures of a particular concept, and models with different plausible causal relations between the represented concepts (called latent variables) are compared to see which model accounts for the most variability in the data. Other structural equation work leads to the conclusion that the embedded-processes model's focus of attention needs to be considered as one latent variable to capture individual differences in performance on a wider variety of tasks ( Gray et al., 2017 ).

The Purpose-of-Model Continuum

The bottom panel of Figure 2 shows a continuum of some working memory models on the basis of their purposes of study. To the left are models that have taken most of their direct support from idiographic information and have had as a purpose the prediction of individual differences, such as M. Just and Carpenter's (1992) model and earlier supportive work by Daneman and Carpenter ( 1980 ; see also Daneman & Merikle, 1996 ). Engle's (2002) goal maintenance approach is similar except that it has more often included and relied upon a variety of new experimental procedures producing nomothetic results in support of the theory, along with individual differences. Case et al. (1982) and Gaillard, Barrouillet, Jarrold, and Camos (2011) are examples of using developmental data as extreme individual differences, but developmental groups can be considered an intermediate case inasmuch as researchers comparing these groups do not always make as detailed use of individual differences within an age group. The embedded-processes model falls in the middle of the road, depending sometimes on nomothetic results (e.g., Cowan, Saults, & Blume, 2014 ), other times on developmental differences (e.g., Cowan, Li, Glass, & Saults, 2017 ; Cowan, Ricker, Clark, Hinrichs, & Glass, 2015 ), and yet other times on development along with idiographic results within an age group (e.g., Cowan et al., 2005 ; Cowan, Fristoe, Elliott, Brunner, & Saults, 2006 ). In the multicomponent approach, most of the work has been from the point of view of nomothetic inference, though not wholly without input from idiographic differences, and especially those from cases of brain damage affecting one part of the working memory system or another. Last, on the nomothetic end of the continuum are pioneers, such as James (1890) and Miller (1956) , who wrote when it was not yet possible to consider individual differences as precisely as we can do with modern methods.

Fruits of Recent Research: Convergence of the Models?

Why do theorists disagree? There is some disagreement on actual data, but the difference in theories probably comes more from theorists' attention to one or another aspect of a vast literature; it is difficult to consider all of the research at the same time. If we are doing our science well, though, the models should eventually start to converge on the truth. We are happy to report that we think this convergence is happening; changing models are moving toward one another. A key example is that some versions of the multicomponent model and embedded-processes model are becoming more similar, in both a reconciliation between modularity and attention and a reconciliation between nomothetic and ideographic purposes.

Recent Research Reconciling Modularity and Attention

Although we have presented modularity and attention as separate dimensions of working memory models, there is an intersection between them in that modules supposedly preserve materials with different codes (e.g., verbal and visual–spatial codes) separately, without interference between the two. Attention supposedly allows storage of information from a variety of codes, albeit with the potential for interference between materials from different codes and the potential to prioritize some information at the expense of other information.

A Stand-In for Modularity in the Embedded Processing Approach

In the embedded-processes approach (e.g., Cowan, 1988 ), there is not a set of different modules (like separate verbal and visuospatial buffers), but there is a prediction similar to models that do have modules. It is the prediction that items with similar features interfere with each other more than do items with different features. In a modular model, this feature-specific interference occurs because items with similar features are held in the same store. In the embedded-processes approach, items, regardless of type, are held in the activated portion of long-term memory, but when items with similar features are concurrently held, they interfere with one another because they depend on the same neural apparatus for that kind of feature. The question for this approach has been how much information is held in the focus of attention versus in the activated portion of long-term memory.

Cowan (1988 , 2001) and Saults and Cowan (2007) tended toward the assumption that most information was held in the focus of attention when rehearsal, chunking, grouping processes could not play a role. Further research, however, has led to the changed assumption that, although several items at a time are at first represented in the focus of attention together, they can be quickly off-loaded to the activated portion of long-term memory to free up the attention for other work. Specifically, Cowan et al. (2014) carried out a number of experiments in which a series of verbal items (spoken or printed) were presented along with a spatial array of visual objects. The sets were presented one after another in either order, and participants were required to repeat a single word ( the ) to prevent verbal rehearsal. In some blocks of trials, the task was to remember both sets, and there was a recognition item coming from one set or the other. In other blocks of trials, the task was to remember just one set (verbal in some blocks, nonverbal in others). Using data from these trial types, it was possible to estimate that about two verbal and two visual items could be retained regardless of whether one or both modalities had to be remembered. On top of that, approximately another one item could be retained, with that central capacity devoted to one modality or split between modalities, depending on the trial type. The explanation was that the focus of attention does not continually retain more than a single item at a time; it may take in and then off-load one set in order to be ready for the second set. The approximately one-item, shared capacity limit may occur for a variety of reasons, such as the need to attend periodically to sets of information in the activated portion of long-term memory in order to refresh or improve the representations. Any such function that would have to be divided between two sets when both of them have to be retained, so refreshing one set comes at the expense of refreshing the other.

The Focus of Attention in the Multicomponent Approach

In the multicomponent modeling approach to working memory, the main role of attention has traditionally been to operate through the central executive to help control cognition. In the current model of A. Baddeley (2000) , another possible role is to preserve information via the episodic buffer, which might serve the same role as the focus of attention in Cowan's model (see A. Baddeley, 2001 ). It is therefore perhaps not surprising that, in recent years, Baddeley, Hitch, and colleagues have investigated the focus of attention and, in particular, priority given to some items in a list at the expense of other list items ( Allen, Baddeley, & Hitch, 2017 ; Hu, Allen, Baddeley, & Hitch, 2016 ). In these studies, the number of points awarded for recall is set to be greater for some items than for others. There is automatic priority to the last list item, and in addition, participants appear able to prioritize at least one other list item at the expense of other items. Prioritization cannot be simply a matter of encoding of the information, inasmuch as priorities can be set even after the memoranda have disappeared from the computer screen (e.g., Cowan & Morey, 2007 ; Griffin & Nobre, 2003 ).

Modularity, Attention, and Brain Research

The interplay between the concepts of attention as a storage device versus nonattentional, possibly specialized storage modes in working memory is a popular theme in recent neuroscientific research on working memory ( Cowan et al., 2011 ; Lewis-Peacock, Drysdale, Oberauer, & Postle, 2012 ; Li, Christ, & Cowan, 2014 ; Majerus et al., 2016 ; Reinhart & Woodman, 2014 ; Rose et al., 2016 ; Wallis, Stokes, Cousijn, Woolrich, & Nobre, 2015 ). These neuroimaging studies point to an area in parietal cortex, the intraparietal sulcus, as particularly important in indexing the items held with the help of the focus of attention, whereas the actual neural representation of information in working memory is seen not there but in posterior cortical areas close or identical to the areas in which the initial processing of the information took place. These posterior areas appear to reflect the activated portion of long-term memory or, by another view, modular memory stores along with a parietally based focus of attention, whereas central executive control processes appear to rely more heavily on frontal areas. There have thus been leaps in the quest to understand the neural underpinnings of attention-based and attention-free aspects of working memory.

Summary: Reconciling Modularity and Attention

Across theorists from the multicomponent and embedded-processes camps, there is increasing convergence of their ideas. The embedded-processes camp acknowledges limitations in how much attention is used directly to store information, whereas the multicomponent camp now acknowledges a role of the focus of attention. Still, there are theorists who advocate full modularity ( Logie, 2016 ) or the full use of attention ( Morey & Bieler, 2013 ). With recent technological advances, these mechanisms can be explored more deeply on a neural level.

Recent Research Reconciling Nomothetic and Ideographic Approaches

During most of the history of working memory research, nomothetic and ideographic approaches have relied on somewhat separate methods. Nomothetic researchers have emphasized careful task analyses, as in most of the research reported by A. D. Baddeley and Hitch (1974) . In contrast, ideographic researchers have needed to rely on somewhat standardized tests to examine individual differences in abilities, as in the applications of working memory tests to an understanding of good and poor readers by Daneman and Carpenter ( 1980 ; cf. Daneman & Merikle, 1996 ). In contrast, in recent work on individual differences, careful analyses of certain tasks have proved to be critical for an understanding of individual differences.

Consider, for example, the structural equation modeling work of Gray et al. (2017) , fit to 9-year-old children to account for individual differences in performance on a battery of working memory tasks (verbal, spatial, and visual tasks with standard and running span methods). To understand the results, a key task analysis on the basis of past nomothetic work was an analysis of performance on a running digit span task. In each running span trial, participants received a long list of spoken digits without knowing when the list would end. The task was to wait for the end of the list and then recall a small number of the items from the end of the list. There is evidence Gray et al. reviewed that this task is difficult because rehearsal and grouping are not possible (given the long list length and unpredictability regarding when the list will end), making the use of attention critically important for this task. According to other studies that Gray et al. reviewed, nonverbal visual materials also critically require attention for maintenance, whereas rehearsable and groupable verbal materials require less attention. Gray et al. found that list memory tasks with verbal materials were intercorrelated well except for the running verbal span task, running digit span, which, instead, was best intercorrelated with the visual and spatial tasks. This anomaly was resolved using a model in which one latent variable was the focus of attention, subsuming running span along with the visual and spatial tasks. To provide the best fit, the multicomponent model had to be modified to be more like the embedded-processes model, replacing the visual–spatial store with storage in the focus of attention. Thus, the task analysis from previous nomothetic work contributed to an understanding of individual differences in working memory.

The nomothetic analysis of many tasks by Unsworth and Engle (2007) indicated that there is no fundamental difference between short-term memory tasks that required only storage and working memory tasks that required both storage and processing. It was aspects of both kinds of tasks requiring the control of attention that distinguished between better and poorer performers, no matter whether attention was needed for maintenance, manipulation, or long-term retrieval. As a final example of nomothetic analysis contributing to ideographic knowledge, Friedman et al. (2006) used an analysis of executive function into three more specific functions and found that updating of information in working memory correlated with intelligence; shifting of the focus of attention and inhibition of irrelevant material did not.

The examination of different groups is an important part of the ideographic approach, and it, too, benefits from careful task analyses in recent work. Cowan (2016 , 2017b) summarized work addressing the question of what accounts for developmental differences in working memory. Task analyses were used to equate participants in age groups from the early elementary school years to adulthood on various confounding processes, such as the ability to ignore distracting items, the ability to rehearse, and familiarity with the information. In every case, working memory still increased with age even with these factors equated. Cowan et al. (2017) , however, learned more by carrying out a study comparable to the adult work by Cowan et al. (2014) , requiring working memory maintenance of both arrays of colored spots and series of tones. They found that what improved with development was not the ability to hold more information in the focus of attention but the ability to preserve more information from each modality in a manner making it more resistant to cross-modality interference. Perhaps, it is the ability to off-load information efficiently to the activated portion of long-term memory that improves with development. In another example, coming from the view that what distinguishes younger versus more mature participants is the speed at which items can be mentally refreshed in working memory. Gaillard et al. (2011) carefully manipulated materials to equate that speed across age groups. In that way, they were able to equate working memory performance levels across age groups. Combining these studies, we do not yet know whether working memory increases with age because processing speed increases allow faster refreshing of items before they decay or, conversely, whether processing speed increases because a larger working memory capacity allows more items to be refreshed in parallel ( Lemaire, Pageot, Plancher, & Portrat, 2017 ). Moreover, what is called refreshing might actually be the successful off-loading of information out of the focus of attention and into activated long-term memory. It could entail the use of attention to discover and memorize structure, forming groups and chunks and, thereby, reducing the rate of forgetting.

Summary: Reconciling Nomothetic and Ideographic Approaches

A very exciting development in the field of working memory is the coming together of careful task analysis and nomothetic information and the examination of individual and group differences, all in the same studies. In 2017, Randall Engle gave the keynote address for the annual Psychonomic Society meeting essentially on the basis of the premise that there has been a long-standing need to merge the nomothetic and ideographic approaches, which he illustrated in the field of working memory.

Conclusion: Potential New Directions for Language Research

The theories of working memory are leading us closer to an understanding of the extent to which language, like other information, is retained in separate modules versus a common problem-solving space, how much it depends on attention as opposed to automatic processing, and how much it can benefit from ideographic and nomothetic experimentation. In future work, we might anticipate that a new understanding of these themes in the field of working memory can be applied to more connected discourse. For example, we might learn more about the role of attention and working memory in the misinterpretation of sentences (e.g., Patson et al., 2009 ) and learn who is most susceptible to these misinterpretations and under what conditions. We might learn whether the degree of modularity (or nonmodularity) is similar for working memory and language. Finally, we might learn what language mechanisms change with normal and abnormal development and how individual differences in language may depend on working memory capabilities. In short, the field is thriving.

Acknowledgments

This work was completed with support from National Institutes of Health Grant R01 HD-21338 to Cowan.

Funding Statement

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Scott H Young

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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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[65] Thalmann, M., Souza, A. S., & Oberauer, K. (2019). How does chunking help working memory? Journal of Experimental Psychology: Learning Memory and Cognition, 45(1), 37–55. https://doi.org/10.1037/xlm0000578

[66] Standing, L., Conezio, J., & Haber, R. N. (1970). Perception and memory for pictures: Single-trial learning of 2500 visual stimuli. Psychonomic Science, 19(2), 73–74. https://doi.org/10.3758/BF03337426

[67] Gobet, F. (2005), Chunking models of expertise: implications for education. Appl. Cognit. Psychol., 19: 183-204. doi:10.1002/acp.1110

[68] Ericsson, K. A., & Kintsch, W. (1995). Long-term working memory. Psychological Review, 102(2), 211–245.

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

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

[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.

[72] 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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[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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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 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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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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• Word Count 496
• Page Count 2
• Subject Psychology

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Home — Essay Samples — Nursing & Health — Human Brain — Review on Working Memory

Review on Working Memory

• Categories: Human Brain

About this sample

Words: 439 |

Published: Jan 15, 2019

Words: 439 | Page: 1 | 3 min read

Works Cited

• Baddeley, A. D. (2000). The episodic buffer: A new component of working memory? Trends in cognitive sciences, 4(11), 417-423.
• Baddeley, A. D., & Hitch, G. (1974). Working memory. Psychology of learning and motivation, 8, 47-89.
• Cowan, N. (2010). The magical mystery four: How is working memory capacity limited, and why? Current Directions in Psychological Science, 19(1), 51-57.
• Daneman, M., & Carpenter, P. A. (1980). Individual differences in working memory and reading. Journal of verbal learning and verbal behavior, 19(4), 450-466.
• Ericsson, K. A., & Kintsch, W. (1995). Long-term working memory. Psychological review, 102(2), 211-245.
• Fukuda, K., & Vogel, E. K. (2011). Individual differences in recovery time from attentional capture. Psychological science, 22(3), 361-368.
• Miller, G. A. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychological Review, 63(2), 81-97.
• Unsworth, N., Fukuda, K., Awh, E., & Vogel, E. K. (2014). Working memory and fluid intelligence: Capacity, attention control, and secondary memory retrieval. Cognitive psychology, 71, 1-26.
• Vogel, E. K., & Machizawa, M. G. (2004). Neural activity predicts individual differences in visual working memory capacity. Nature, 428(6984), 748-751.
• Weidler, B. J., & Loehlin, J. C. (1993). Individual differences in working memory capacity and fluid intelligence: A multi-trait-multi-method analysis. Intelligence, 17(2), 107-117.

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Working Memory Model - 8 Mark Example Answer

Subject: Psychology

Age range: 16+

Resource type: Assessment and revision

Last updated

29 October 2019

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This is an example answer for an 8 Mark Extended Essay question “Evaluate the Working Memory Model”

This is a question taken from one of the final external exams from the Edexcel A Level Psychology Specification (2015).

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