working memory essay

Working Memory Model (Baddeley and Hitch)

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

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.

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The Role of Working Memory in the Writing Process

High school teachers can guide students to success in writing assignments by structuring tasks to account for working memory.

In high school, reflection essays, analysis papers, and literature reviews for English and other courses supplement more traditional summaries and narratives. Regardless of the focus, we’re familiar with the complicated writing process, which requires brainstorming, organizing, and translating ideas into words while using correct mechanics (punctuation, grammar, sentence structure, etc.). At the same time, writing a coherent and well-developed piece requires valuable working memory. Unfortunately, subtle working memory issues may increase these complex writing challenges.

Writing demands working memory capacity, retention time, and processing depth. For example, gaps in remembering and understanding information slow the process of manipulating and translating information. As a result, students may prematurely discard information they need. How can we engage students in maximizing their working memory functioning throughout the writing process?

Consider the following strategies: increasing capacity through note-taking, deepening processing with discussion and summarization, and extending retention time with review and revisions.

Setting Up a Writing Task to Account for Working Memory

Analyzing the writing task: Analyzing the assignment and identifying discrete steps creates a structure in working memory, easing the mental organization process. While doing this with your class, ask students for examples of relevant information. For example, if they are analyzing the Napoleonic Era, ask them to provide two decisions Napoleon made that led to his defeat. Examples provide students with brain priming and enable you to assess retention and comprehension. In this way, task analysis serves as a confirmation of students’ understanding of directions and their content knowledge.

Consider the following strategies: intermittent low-stakes testing to support remembering and understanding, student-generated teach-backs for knowledge review and rehearsal, student partnerships for reading directions, and use of step-by-step checklists.

Prewriting: Now that students have created a mental organization framework, they can begin writing. A structured approach is essential when considering the extensive working memory demands. For example, creating an organizer provides a review of information, thus increasing the depth of working memory processing. This way, information is more efficiently organized for easy long-term memory storage. Thus, rather than taxing working memory capacity, information can be accessed more easily from long-term memory as needed.

Start by activating prior knowledge with a 5- to 10-minute brainstorm. Then create an overall structure of subtopics, main ideas, and their logical connections, using outlines, mind maps, graphic organizers, or note cards.

Leave time between creating the organizer and revising it to allow for mental organization of the information and increased objectivity. During the revision, have students use notes to identify possible gaps. Be sure to recognize the need for processing time to facilitate decision-making. Avoid fatigue by establishing a work session of an hour at most, such as 45 minutes of focused work, a 5-minute break for processing, and a 10-minute review.

Planning: Executive functions such as attention, inhibition, and emotional regulation impact working memory functioning. Therefore, planning is a proactive step that can help students overcome future obstacles. Partner students to expand the writing process checklist they created during task analysis.

For example, have students enter work session appointments with alerts into a digital calendar. Have them enter interim due dates with a specific action step for receiving feedback. Finally, a growth step would be to include step-specific time estimates to encourage the development of accurate planning.

Translating ideas into words: Translating ideas into words requires self-regulation. Decisions regarding word choice, spelling, and grammar require persistence. Therefore, avoiding internal distractions impacts working memory’s ability to manipulate and organize information.

Have students consider the following strategies:

• Cover everything in the organizer except the section guiding their current writing.
• Lessen cognitive and physical demands with speech-to-text.
• Write without editing by turning off spell or grammar check features.
• Establish a cueing system to mark words or areas of uncertainty. Try highlighting or italicizing word choice to review, or adding a question mark to indicate uncertainty of ideas.

Editing: Allow at least an hour between writing and editing to let students focus on their actual wording versus what they think they wrote. Time also offsets the emotional attachment to their words. Finally, lessen the chances of students feeling overwhelmed by limiting editing to one or two specific areas. Their editing checklist might focus on writing mechanics, specialized vocabulary, or places they flagged as unclear during writing. Either partner students or consider using text-to-speech to ensure accurate reading of their draft.

Reflecting: Reflection provides a review of the student’s writing process. Emphasizing their goals and gains moves them from working memory to long-term memory.

To reinforce growth, ask students to identify a gain. Then establish a goal by focusing on a feedback suggestion. For example, perhaps they struggled to hold information in their working memory while writing an English essay. Ask them to identify a strategy, technology, or resource that would support their ability to decide what information to include in a future organizer.

When working memory is functioning effectively and efficiently, the complex demands of writing become steps in a workable process rather than obstacles of frustration.

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Article contents

Working memory.

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

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

• short-term memory
• serial order
• intelligence

Introduction

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

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

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

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

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

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

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

Temporal Limits

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

Capacity Limits

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

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

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

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

Limiting Mechanisms

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

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

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

Substructure and Relationship to Other Aspects of Cognition

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

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

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

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

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

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

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

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

Empirical Investigation and Key Findings

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

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

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

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

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

Set Size and Retention Interval Effects

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

Primacy and Recency Effects

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

Errors and Effects of Similarity

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

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

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

Individual Differences and Links With Other Facets of Cognition

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

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

The Importance of Working Memory

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

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

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

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

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

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

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

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June 5, 2017

Working Memory: How You Keep Things “In Mind” Over the Short Term

Given its central role in our mental life working memory may become important in our quest to understand consciousness itself

By Alex Burmester & The Conversation US

Ben Pipe Photography Getty Images

The following essay is reprinted with permission from  The Conversation , an online publication covering the latest research.

When you need to remember a phone number, a shopping list or a set of instructions, you rely on what psychologists and neuroscientists refer to as working memory. It’s the ability to hold and manipulate information in mind, over brief intervals. It’s for things that are important to you in the present moment, but not 20 years from now.

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Researchers believe working memory is central to the functioning of the mind. It correlates with many more general abilities and outcomes—things like  intelligence  and  scholastic attainment —and is linked to basic sensory processes.

Given its central role in our mental life, and the fact that we are conscious of at least some of its contents, working memory may become important in our quest to understand consciousness itself. Psychologists and neuroscientists focus on different aspects as they investigate working memory: Psychologists try to map out the functions of the system, while neuroscientists focus more on its neural underpinnings. Here’s a snapshot of where the research stands currently.

How much working memory do we have?

Capacity is limited—we can keep only a certain amount of information “in mind” at any one time. But researchers debate the nature of this limit.

Many suggest that working memory can store a  limited number of “items” or “chunks” of information . These could be digits, letters, words or other units. Research has shown that the number of bits that can be held in memory can depend on the type of item—flavors of ice cream on offer versus digits of pi.

An alternative theory suggests working memory acts as a  continuous resource  that’s shared across all remembered information. Depending on your goals, different parts of the remembered information can receive different amounts of resource. Neuroscientists have suggested this resource could be  neural activity , with different parts of the remembered information having varying amounts of activity devoted to them, depending on current priorities.

A different theoretical approach instead argues that the capacity limit arises because different  items will interfere with each other in memory .

And of course memories decay over time, though rehearsing the information that’s in working memory seems to mitigate that process. What researchers call maintenance rehearsal involves repeating the information mentally without regard to its meaning—for example, going through a grocery list and remembering the items just as words without regard to the meal they will become.

In contrast, elaborative rehearsal involves giving the information meaning and associating it with other information. For instance, mnemonics facilitate elaborative rehearsal by associating the first letter of each of a list of items with some other information that is already stored in memory. It seems only elaborative rehearsal can help consolidate the information from working memory into a more lasting form—called long-term memory.

In the visual domain,  rehearsal may involve eye movements , with visual information being tied to spatial location. In other words, people may look at the location of the remembered information after it has gone in order to remind them of where it was.

Working memory versus long-term memory

Long-term memory is characterized by a much larger storage capacity. The information it holds is also more durable and stable. Long-term memories can contain information about episodes in a person’s life, semantics or knowledge as well as more implicit types of information such as how to use objects or move the body in certain ways (motor skills).

Researchers have long regarded working memory as a  gateway into long-term storage . Rehearse information in working memory enough and the memory can become more permanent.

Neuroscience makes a clear distinction between the two. It holds that working memory is related to temporary activation of neurons in the brain. In contrast, long-term memory is thought to be related to physical changes to neurons and their connections. This can explain the short-term nature of working memory as well as its greater susceptibility to interruptions or physical shocks.

How does working memory change over a lifetime?

Performance on tests of working memory improves throughout childhood. Its capacity is a major driving force of cognitive development. Performance on assessment tests increase steadily  throughout infancy ,  childhood and the teenage years . Performance then reaches a peak in young adulthood. On the flip side, working memory is one of the cognitive abilities most sensitive to aging, and performance on  these tests declines in old age .

The rise and fall of working memory capacity over a lifespan is thought to be related to the normal development and degradation of the prefrontal cortex in the brain, an area responsible for  higher cognitive functions .

We know that damage to the prefrontal cortex causes working memory deficits (along with many other changes). And recordings of neuronal activity in the prefrontal cortex show that  this area is active during the “delay period”  between when a stimulus is presented to an observer and when he must make a response—that is, the time during which he’s trying to remember the information.

Several mental illnesses, including  schizophrenia and depression , are associated with decreased functioning of prefrontal cortex, which can be  revealed via neuroimaging . For the same reason, these illnesses are also associated with decreased working memory ability. Interestingly, for schizophrenic patients, this deficit appears  more marked in visual rather than verbal  working memory tasks. In childhood, working memory deficits are linked to  difficulties in attention, reading and language .

Working memory and other cognitive functions

The prefrontal cortex is associated with a wide array of other important functions, including  personality, planning and decision-making . Any decrease in the functioning of this area is likely to affect many different aspects of cognition, emotion and behavior.

Critically, many of these prefrontal functions are thought to be intimately linked to, and perhaps dependent on, working memory. For instance, planning and decision-making require us to already have “in mind” the relevant information to formulate a course of action.

A theory of cognitive architecture, called  Global Workspace Theory , relies on working memory. It suggests that information held temporarily “in mind” is part of a “global workspace” in the mind which connects to many other cognitive processes and also determines what we are conscious of in any given moment. Given that this theory suggests working memory determines what we are conscious of, understanding more about it may become an important part of solving the mystery of consciousness.

Improving your working memory

There is some evidence that it’s possible to train your working memory using interactive tasks, such as simple games for children that involve memory ability. It has been suggested that this training can help improve scores on other types of tasks,  such as those involving vocabulary and mathematics . There is also some evidence that training to beef up working memory can  improve performance for children with specific conditions , such as ADHD. However, research reviews often conclude that benefits are  short-lived and specific to the trained task .

Furthermore, the enhancements found in some of these studies could be due to learning how to more efficiently use one’s working memory resources, as opposed to increasing its capacity. The hope for this kind of training is that we can find relatively simple tasks which will both improve performance not just on the task itself but also transfer to a range of other applications.

This article was originally published on  The Conversation . Read the original article .

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Working Memory and Attention – A Conceptual Analysis and Review

Klaus oberauer.

1 University of Zurich, CH

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

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

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

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

A second distinction pertains to what we attend to. I find it useful to distinguish the possible objects of attention along two dimensions (see Table ​ Table1 1 ). 1 First, we can distinguish between attention to our currently perceived environment (e.g., attention to visual objects or auditory streams) from attention to information currently not perceived, such as attention to remembered episodes or concepts that we think about. 2 Second, we can distinguish between attention to things and events in the world around us on the one hand, and attention to our own goals and (mental or overt) actions on the other. The latter form of attention includes selection of our current goal or task set and shielding it from distraction ( Kane & Engle, 2003 ; Monsell, 2003 ), selection of one of several possible actions ( Pashler, 1994 ), and monitoring of our actions and their outcomes ( Yeung, Botvinick, & Cohen, 2004 ).

A Taxonomy of Attention.

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

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

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

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

Attention as a Resource

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

Attention for Storage and Processing

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

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

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

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

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

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

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

Attention for Perception and Memory

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

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

Controlled Attention

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

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

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

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

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

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

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

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

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

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

Attention as Selection

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

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

How is Information Selected into Working Memory?

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

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

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

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

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

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

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

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

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

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

How is Information selected within WM?

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

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

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

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

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

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

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

What is the Relation between WM and Perceptual Attention?

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

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

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

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

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

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

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

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

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

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

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

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

Conclusions

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

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

Unsurprisingly, there are also many things we don’t know. Table ​ Table2 2 presents a non-exhaustive list of open questions that I believe future research should address with high priority. I hope that this effort will lead to an increasingly more precise and nuanced picture of how WM is related to attention.

Open Questions.

Funding Statement

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

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

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

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

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

Ethics and Consent

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

Funding Information

Competing interests.

The author has no competing interests to declare.

Scott H Young

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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

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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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[82] Ramirez, G., Hooper, S. Y., Kersting, N. B., Ferguson, R., & Yeager, D. (2018). Teacher Math Anxiety Relates to Adolescent Students’ Math Achievement. AERA Open. https://doi.org/10.1177/2332858418756052

[83] Strack, J., Lopes, P., Esteves, F., & Fernandez-Berrocal, P. (2017). Must We Suffer to Succeed? Journal of Individual Differences, 38(2), 113–124. https://doi.org/10.1027/1614-0001/a000228

[84] Brooks, A. W. (2014). Get excited: reappraising pre-performance anxiety as excitement. Journal of Experimental Psychology. General, 143(3), 1144–1158. https://doi.org/10.1037/a0035325

[85] Park, D., Ramirez, G., and Beilock, S. L. 2014. The role of expressive writing in math anxiety. J. Exp. Psychol. Appl. 20:103–11. doi:10.1037/xap0000013

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Working Memory

Reviewed by Psychology Today Staff

Working memory is a form of memory that allows a person to temporarily hold a limited amount of information at the ready for immediate mental use. It is considered essential for learning, problem-solving, and other mental processes.

On This Page

• How We Use Working Memory
• How Working Memory Works

Making use of working memory is like temporarily pinning up certain images or words to a board so they can be moved or manipulated in some other way. The ability to keep certain details “at hand,” including those we haven’t committed to long-term memory, supports a variety of day-to-day mental functions.

Recalling the earlier part of a sentence to understand a later part, holding a number in mind while doing a math problem in one’s head, remembering where an object was just seen, and keeping multiple concepts in mind in order to combine them have been described as examples of working memory.

Working memory is believed to support many kinds of mental abilities at a fundamental level. It allows one to retain multiple pieces of information for use in the moment, which is essential to activities from reading or having a conversation to learning new concepts to making decisions between different options.

While long-term memory can store a huge amount of information, the amount of details contained for ready usage in working memory is thought to be relatively limited. There are differing models of the working memory system. Some have argued that it includes multiple components that handle different kinds of information and are distinct from long-term memory. Others propose that working memory represents a part of long-term memory that is especially activated and a smaller part that is the focus of attention.

Though the limits are debated, some scientists have suggested that when people aren’t able to use tactics like repeating details out loud, they may be able to keep just a few items in focus at a time. Those items can be simple or complex—including individual letters or numbers to be remembered as well as larger “chunks” of information (such as acronyms like “USA” and “UK,” and even more complex concepts).

Virtually everyone seems to put working memory to work throughout the day, but the performance of this memory system (or “working memory capacity”) is stronger in some individuals than in others—with implications for a person’s ability to learn and function.

The representation of different kinds of information (such as visual or or verbal details) in working memory seems to depend on parts of the cerebral cortex that are involved in the perception and long-term memory of those kinds of information. The prefrontal cortex, a part of the brain linked to cognitive control, is thought to play a key role in managing the current contents of working memory, regardless of type.

Research shows that measures of an individual’s working memory ability are strongly related to measures of intelligence—particularly an aspect called fluid intelligence, which is involved in solving novel problems.

Measures of working memory suggests that it typically improves throughout childhood. Working memory tends to decline in older age, research suggests—though it may begin to gradually decrease after early adulthood. It has been proposed that later working-memory decline may help account for age-related declines on other kinds of cognitive tasks.

Individual differences in working memory ability can be assessed using a range of tasks. Among them are “working memory span” tasks in which a person tries to, for example, read through sentences while remembering particular words from each. Another type of measure is an “n-back” task, in which one sees or hears a sequence of items and has to indicate when the current item matches a previous one. In a 2-back version, for instance, if the letters P, S, T, H, A, F were followed by an A, one would indicate that it matched what came two letters back. N-back task performance doesn’t necessarily correlate strongly with performance on other working memory tasks, and they may measure different components of working memory.

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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! 

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Reference this article:

About The Author

Memory Topics:

Free Memory Test

Serial Position Effect

Primacy Effect

Recency Effect

Short Term Memory

Sensory Memory

Working Memory

Long Term Memory

Episodic Memory

Semantic Memory

False Memories

Photographic Memory

Memory Tricks

Memory Palace

Rote Memorization

Atkinson and Shiffrin Model

Proactive Interference

Retroactive Interference

State Dependent Memory

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What is working memory.

Understanding our mental ‘scratchpad’

Writer: Rae Jacobson

Clinical Experts: Matthew Cruger, PhD , Linda Hecker, MEd

What You'll Learn

• What is working memory?
• What is the connection between learning disorders, ADHD and working memory?
• What are signs a child needs help with working memory?

Kids who have a hard time “staying on track” might have problems with something called working memory. Working memory is an executive function that plays a big role in how we process, use and remember information on a daily basis. Remembering a phone number, recalling directions, or writing an essay are all tasks that use working memory.

Unlike long-term memories that stay even we’re not thinking of them, working memory is more like a mental scratchpad holding all the information we need. Everyone struggles with working memory sometimes. We all forget an item from a shopping list or draw a blank trying to remember the rules of a game. But for kids with learning disorders, working memory can be a bigger problem.

This is because children with learning disorders or ADHD are already using more of their “scratchpad.” For example, a child with auditory processing issues has to work harder to listen to and remember what’s being said in class. Kids with ADHD have to actively work to stay focused and organized — things that tend to be simple for other children.

Kids who have trouble with working memory often make mistakes. They might struggle to follow directions. Or forget to finish homework assignments and chores. It can be easy for teachers and parents to get frustrated. But knowing that a child is having a hard time with working memory will make it easier to get them the help they need.

Imagine this: You’re throwing a party and ask your child to help set up. The instructions you give seem simple enough: Put you toys in your room, move everyone’s shoes to the closet and set the table. He agrees, but when you go check on him later, the table isn’t set, his shoes are still in the hallway and he’s put toys … in the closet.

What’s going on?

Kids who have a hard time “staying on track” may be having problems with working memory, which is an executive function that plays a major role in how we process, use and remember information on a daily basis. Remembering a phone number, recalling directions, remembering how to use grammar and structure, writing an essay and applying the quadratic formula are all mental tasks that use working memory.

“Working memory is sort of a category above attention,” says Matthew Cruger, PhD, a clinical neuropsychologist in the Learning and Development Center at the Child Mind Institute. “It’s keeping in mind anything you need to keep in mind while you’re doing something.” Whereas long-term memories stay with us even when we’re not thinking of them, working memory is an active process — a mental scratchpad where we hold and process all the information we need to access at any given time.

A limited workspace

But what happens when the scratchpad gets overloaded?

“Our brains have a finite capacity for juggling a lot of information at once,” explains Linda Hecker, MEd , the lead education specialist at the Landmark College Institute for Research and Training. Hecker, who also serves as an associate professor at the college, says she helps her students understand the role of working memory by describing it as a table. “We talk about it as ‘cognitive workspace.’ When you have a lot of new information it’s easy to overload your cognitive workspace and things start falling off.”

Dr. Cruger agrees, noting that when kids with working memory issues are asked to perform a new task and think of five rules for how the task should be done, they often come up short. “They can’t hold both sets of directions in mind at once. They end up doing the task, but making a lot of mistakes along the way — or only completing half the assignment — because they’re not able to keep in mind what they need to do, what comes next and the rules for how it’s done all at once.”

Learning disorders and working memory

Everyone struggles with the limits of working memory sometimes — forgetting an item from a shopping list, or drawing a blank when you’re trying to remember the rules of a new game. But for kids with learning disorders , Hecker says, working memory often presents a more significant problem.

“Kids with LDs have smaller working memory capacity,” says Hecker, because adjusting for the difficulties that come with LDs — like dyslexia , nonverbal learning disorder or auditory processing issues — takes up a considerable amount of their “cognitive workspace.”

That’s because they need to consciously break down and perform processes that other kids do automatically. For example:

• If a child has auditory processing issues they have to work much harder to listen, recall and apply what’s being said in class.
• A kid with a non-verbal learning disorder has to actively work to appropriately interpret and respond to social cues – like facial expressions, sarcasm and implication — a process that’s second nature for most kids.

This extra work means more clutter on the “table,” which leaves less space for new information and often translates to a slower processing speed overall.

ADHD and working memory

Kids with ADHD can also struggle with working memory, which is one of the core executive functions — the mental skills responsible for helping us stay organized, set goals and complete them. Weaknesses in executive functions are what make kids with ADHD prone to being disorganized as well as being inattentive. Like learning disorders, kids with ADHD have to actively work to stay focused and organized — things that tend to be automated for other children.

For example, keeping guiding rules or principles in mind is more difficult for kids with ADHD who are already having trouble tuning out distractions. They might be external distractions, such as a dripping faucet or kids playing outside, or internal, like anxiety or even just wondering what’s for dinner later.

“A smaller cognitive workspace means that working memory functions — holding on to information, recalling instructions or following through with tasks that require planning — are harder to perform,” says Hecker. “Less space means things are more likely to get lost along the way.”

One of the challenges kids with working memory issues face, Dr. Cruger notes, is that their lapses can easily be misinterpreted as bad behavior. When they fail to follow a set of instructions they appear to be unmotivated or even oppositional which can lead to conflict with teachers and parents and accusations of not trying hard enough. Kids hate having to admit that they can’t remember things, he adds, and they tend to try to minimize the amount of effort they put into things that don’t yield positive results. And the criticism they get in turn is a disincentive for them to expend the extra energy it takes for them to keep track of what’s expected of them.

For example, explains Dr. Cruger, “If you say to your child, ‘Go put your pajamas on, put out your clothes for tomorrow and brush your teeth,’ but he either only completes one or two of the actions, or keeps coming back asking ‘what was the third thing again?’” Without context, it might seem like your child is being disobedient, but once you know what to look for, “it’s a pretty obvious sign he’s struggling with working memory.”

For ways to support kids with working memory challenges, see How to Help Kids With Working Memory Issues.

Frequently Asked Questions

Working memory is an executive function that plays a big role in how we process, use, and remember information. Remembering a phone number, recalling directions, or writing an essay are all tasks that use working memory.

Some signs of problems with working memory include issues following directions, staying organized, and seeing tasks through to completion.

Yes, kids with learning disorders like ADHD, dyslexia, or non-verbal learning disorder often have problems with working memory.

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201 Memory Research Topics & Essay Examples

Memory is a fascinating brain function. Together with abstract thinking and empathy, memory is the thing that makes us human.

❓ Memory Research Questions

🏆 best memory topic ideas & essay examples, 💭 exciting memory research topics, 💫 interesting memory topics for essays, 👍 research topics about memory in psychology, 🕑 learning & memory research topics, 💡 easy memory essay ideas.

In your essay about memory, you might want to compare its short-term and long-term types. Another idea is to discuss the phenomenon of false memories. The connection between memory and the quality of sleep is also exciting to explore.

If you’re looking for memory topics to research & write about, you’re in the right place. In this article, you’ll find 174 memory essay topics, ideas, questions, and sample papers related to the concept of memory.

• How does sensory memory work?
• How is short-term memory different from long-term memory?
• What memory-training techniques are the most effective?
• What are the reasons for memory failures?
• Memory and aging: what is the connection?
• What are the key types of memory disorders?
• How to improve memory?
• Memory Chart Stages in Psychology For instance, the brain uses the procedural memory to encode procedural skills and tasks that an individual is involved in. The stages of memory are very complex and often pass unrecognized.
• Computer’s Memory Management Memory management is one of the primary responsibilities of the OS, a role that is achieved by the use of the memory management unit.
• Memory for Designs Test The examination of the functioning of the memory of an individual cannot be limited to only one memory test, and as a result, there are a variety of assessments that target the various features of […]
• Improving Memory and Study Power Study power and memory are important aspects of the learning process and improving them is necessary for success. Working the brain is important in improvement of memory and study power.
• “The Sorrow of War” by Bao Ninh: Memory as a Central Idea The image of soldier Kien in The Sorrow of War demonstrates the difficulties of the Vietnamese people before, through and after this war.
• Love and Memory From a Psychological Point of View The commonly known love types include affection, passionate love, friendship, infatuation, puppy love, sexual love, platonic love, romantic love and many other terms that could be coined out to basically describe love.
• Memory Test The two controversies determine the classification of memory depending on the form of information processing that occurs in the brain and the different types of memories in relation to the accessibility.
• Review of Wordfast: Strengths and Weaknesses of This Translation Memory Tool Recognizing the variety of benefits of using Wordfast in the translation process, it should be noted that the use of this ACT program can have a number of unintended negative implications for the quality of […]
• ”The Mystery of Memory” Documentary by Gray & Schwarz The documentary examines the brain’s ability to form and retrieve a memory, highlights the importance of neurobiology, and focuses on the problems of PTSD treatment and neuroscience backwardness, concluding that human memory is still a […]
• Memory, Thoughts, and Motivation in Learning Moreover, using the knowledge acquired from various sources of information, students can interpret the contents of their various environments and apply them to their advantage.
• Stroop Effect on Memory Function The aim of the study was to examine the Stroop effect on memory function of men and women. The aim of the study was to examine Stroop effect on men and women’s cognitive functions.
• The Effect of Sleep Quality and IQ on Memory Therefore, the major aim of sleep is to balance the energies in the body. However, the nature of the activity that an individual is exposed to determines the rate of memory capture.
• Chocolate Consumption and Working Memory in Men and Women In this study, the independent variable was chocolate intake, while the dependent variable was the effect of chocolate on the memory of different genders.
• Music Role in Memory and Learning Processes As such, the study purposed to test the differences in visuospatial abilities between men and women bearing in mind that the former is perceived to demonstrate greater memory capabilities compared to the latter As such, […]
• Rivermead Behavioural Memory Test and Cognistat Rivermead Behavioural Memory Test and the Cognistat are the assessment tools employed by the occupational therapists in order to determine the levels of impairment in their mental function that directly impact the individuals’ executive abilities […]
• Memory by Analogy: Hiroshima Mon Amour It is quite painful to recall the events that took place in Japan during the Second World War in the aftermath of the atomic bombing of the cities of Nagasaki and Hiroshima.
• Memory Strategies Examples and How They Work A good strategy for memory is the one that improves information encoding, necessitates storage of data in a memorable state and enables the mind to easily retrieve information. Indeed, a malfunction in retrieval of stored […]
• Shape Memory Alloys (SMAs) The first mentioning of shape memory materials was with the discovery of martensite in 1890, which was the first step for phenomenal discovery of the shape memory effect.
• Memory Model of Teaching and Its Effectiveness The main objective of the research study was to find out the difference in the effect of the memory model and the traditional method of teaching on students’ performance.
• The Difference Between Females and Males Memory The hippocampus is of importance when it comes to memory formation and preservation and is relatively larger in females than males, giving the females advantage in memory cognition.
• Long and Short Term Memory The procedure of conveying information from STM to LTM entails the encoding and consolidation of information: it is not a task of time; the more the data resides in STM it increases the chances of […]
• Factors of Learners’ and Adults’ Working Memory An individual’s working memory refers to their ability to access and manipulate bits of data in their mind for a short period.
• Statistics: The Self-Reference Effect and Memory After the distraction part was over, the participants were asked to recall the twelve adjectives they rated from a list of 42 words. This brings the question of whether the results would be different if […]
• Memory Mechanisms: Cognitive Load Theory The teacher’s task is not only to give information but also to explain the principles of learning and to work with it.
• The Self-Reference Effect and Memory Accordingly, the analysis has the following hypotheses: the SRE should enhance recognition of words that participants can relate to themselves, and people should feel more confident about their memory under the SRE.
• Henry Molaison and Memory Lessons The case of Henry Molaison serves as a poignant reminder of the complexity of memory and the importance of understanding its various components.
• Memory and Attention as Aspects of Cognition It has specific definitions, such as “consideration with a view to action,” “a condition of readiness involving a selective narrowing or focusing of consciousness and receptivity,” and “the act or state of applying the mind […]
• Intergenerational Trauma and Traumatic Memory The exploration of interconnected issues of intergenerational trauma and traumatic memory in society with historical data of collective violence across the world sensitizes to the importance of acknowledging trauma.
• The Role of Memory Cells in Cellular Immunity Therefore, when a bacterium gets into the body for a second time, the response is swift because the body has fought it before. Thus, a healthy body can recognize and get rid of chronic microorganisms […]
• Psychological Conditions in Addition to Highly Superior Autobiographical Memory The authors, who have many papers and degrees in the field, have noted the features of the brain structure and the differences between HSAM.
• Cognitive Psychology: The Effects of Memory Conformity The experiment’s control conditions did not allow the witnesses to discuss the event seen in the videos, while in the other condition, the witnesses were encouraged to discuss the event.
• Survival and Memory in Music of the Ghosts by Ratner When it comes to individual memory of Teera’s childhood, the author explains the connection between her memories of her father and musical instruments: “Perhaps it’s because as a child she grew up listening to her […]
• Concept for Teaching Memory in Primary School Students Teaching is one of the most demanding and demanding jobs in the world because it is the job that holds the future generation together.
• Draw It or Lose It Memory and Storage Considerations Since the size of the biggest component of this data is known and the additional component can be reasonably estimated, memory for it can be assigned at load time.
• The Multi-Storage Memory Model by Atkinson and Schiffrin The function of the is to track the stimuli in the input register and to provide a place to store the information coming from the LTS.
• Emotions: The Influence on Memory At the same time, the influence of positive and negative feelings on the process of memorization and reproduction is different. In conclusion, it should be said that the process of the influence of emotions on […]
• Civility, Democracy, Memory in Sophocles’ Antigone In Sophocles’ Antigone, the narrative flow makes the audience empathize with the tragic fate of the characters, deepening the emotional involvement of the readers and viewers.
• The Psychological Nature of Memory Using the numerical representation of the participants’ results, the researchers calculated the dependence of the memory and theory of mind in the process of recalling the interlocutors.
• Functioning of Human Memory Schemas Consecutively, the study aimed to identify the relation between the facilitation of prior knowledge schemas and memories and the ability to form new schemas and inferences in older adults.
• Enhancing Individual and Collaborative Eyewitness Memory Considering the positive results of research utilizing category clustering recall and the reported benefits of group memory, a question arises whether the use of category clustering recall might diminish the negative effects of group inhibition.
• Memory: Its Functions, Types, and Stages of Storage First, information is processed in sensory memory, which perceives sensory events for a couple of seconds to determine whether the information is valuable and should be kept for a longer period. As information goes through […]
• The Relationship Between the Working Memory and Non-Conscious Experiences The structure of the proposal follows the logical layout, beginning from the background of the issue through the methodology to problem significance and research innovation.
• Consciousness: The Link Between Working Memory and Unconscious Experience The present study seeks to address the gap in the research regarding the executive function of VWM and consciousness. This study will follow a modified structure of Bergstrom and Eriksson experiment on non-conscious WM to […]
• The Role of Image Color in Association With the Memory Functions Memory is the cornerstone of human cognition that enables all of its profound mechanisms, and the instrument of knowledge acquisition and exchange.
• The Memory Formation Process: Key Issues Hippocampus plays an essential role in the memory formation process because it is the part of the brain where short-term memories become long-term memories.
• Information Processing and Improving Learning and Memory Information processing theory is a method of studying cognitive development that arose from the American experimental psychology tradition.
• Memory Techniques in Learning English Vocabulary ‘Word’ is defined by Merriam Webster Dictionary as follows: “1a: something that is said b plural: the text of a vocal musical composition c: a brief remark or conversation 2a: a speech sound or series […]
• Covalent Modification of Deoxyribonucleic Acid Regulates Memory Formation The article by Miller and Sweatt examines the possible role of DNA methylation as an epigenetic mechanism in the regulation of memory in the adult central nervous system.
• Repressed Memory in Childhood Experiences The suffering often affects a child’s psychological coping capacity in any respect, and one of the only ways of dealing with it is to force the memory out of conscious perception.
• Adaptive Memory and Survival Subject Correlation The results of the study have revealed that the participants found it slightly easier to recall the words related to the notion of survival.
• Developmental Differences in Memory Over Lifespan While growth refers to the multiplication of the number of individual units or cells in the body, maturation on the other hand can be defined as the successive progress of the individual’s appendage land organs […]
• Memory, the Working-Memory Impairments, and Impacts on Memory The first important argument for a thorough discussion on how ADHD could affect brain functioning and working memory impairments is the existence of prominent factors that could create a link between the disorder and the […]
• Working Memory in 7 &13 Years Aged Children However, it was hypothesized that children with AgCC will show similar performance improvement in verbal working memory task performance from 7 to 13 years of age as indicated in the study with CVLT.
• Working Memory & Agenesis of the Corpus Callosum However, it was hypothesized that children with AgCC will show similar improvement in performance on verbal working memory task performance from 7 to 13 years of age as indicated in the study with CVLT.
• Lifespan Memory Decline, Memory Lapses and Forgetfulness The purpose of the research by Henson et al.was to deepen the understanding of differential aging of the brain on differential patterns of memory loss.
• Elaborative Process and Memory Performance The process is significant in the study and retention of data. In addition, the application of the concepts in the author’s learning process will be highlighted.
• The Essence of Context Dependent Memory The results ought to show that the context in which eyewitnesses observed an event is important in the recall memory of the participants.
• “Neural Processing Associated With True and False Memory Retrieval” by Yoko The researchers noted that both true and distorted memories activate activities in the left parental and left frontal areas of the brain. Parahippocampal gyrus- Is the area of the brain that is responsible for processing […]
• Dementia and Memory Retention Art therapy is an effective intervention in the management of dementia because it stimulates reminiscence and enhances memory retention among patients with dementia.
• Fabricating the Memory: War Museums and Memorial Sites Due to the high international criticism, a very tiny portion of the East Wing is dedicated to explain the context, yet visitors easily overlook the section after the dense display of tragedies after a-bomb in […]
• Biological Psychology: Memory By and large, there is a general agreement that molecular events are involved in the storage of information in the nervous system. It is about to differentiate different kinds of memory, one which is short-term […]
• The Memory of Silence and Lucy: A Detailed Analysis From damaging relationships to her hope to come back to the native land, Lucy has all kinds of issues to address, but the bigger issue is that Lucy’s progress is cyclical, and she has to […]
• Two Tutorials on the Virtual Memory Subject: Studytonight and Tutorials Point The explanation of the demand paging term leads to the concept of a page fault. It is a phrase that characterizes an invalid memory reference that occurs as a result of a program addressing a […]
• The Relationship Between Memory and Oblivion The purpose of this essay is to discuss the relationship between memory and oblivion, private and public recollection of events, and the way these concepts are reflected in the works of Walid Raad, Christo, and […]
• Music and Memory: Discussion Future research should focus on addressing the limitations of the study and exploring the effect of other types of music. The findings of the study are consistent with the current body of knowledge about the […]
• Fuzzy-Trace Theory and False Memory The writers set out to show the common ground for all these varied scenarios and convincingly show that false memories are a result of an interaction between memory and the cognitive process of reasoning. The […]
• Individual Differences in Learning and Memory In the following paper, the variety of learning styles will be evaluated in relation to theories of human learning and memory retrieval on the basis of the findings currently made by academic researchers.
• The Nature of False Memory Postevent information is one of the reasons that provoke the phenomenon of misinformation. The participants watched a video of a hockey collision and were asked to estimate the speed of the players.
• Organizational Memory and Intellectual Capital The main emphasis here concerns modalities of motivating the retrieval and use of information and experiences in the OM. The source of intellectual capital arises from the managers’ ability to welcome new information and experiences, […]
• Advertising and Memory: Interaction and Effect An advert sticks into one’s memory when it focuses on the characteristic of the material being advertised, other advertisements competing for the same market niche, and the kind of people it targets.
• The Internet and Autobiographical Memory Allie Young’s blog or journal is a perfect illustration of the impact that social sites and blogs have, since for her autobiographic memory; she uses a blog site to write about issues affecting her life.
• Creativity and Memory Effects in Advertising A study was conducted in China to establish the kind of effects agency creativity has on the total outcome of the advertising campaign.
• Memory, Thinking, and Human Intelligence As Kurt exposits, “The effects of both proactive and retroactive inferences while one is studying can be counteracted in order to maximize absorption of all the information into the long-term memory”.
• Psychological Issues: Self-Identity and Sexual Meaning Issues, and Memory Processing Most sex surveys are run by firms dealing in other products and the motives of the surveys are for marketing of their primary products.
• Human Memory as a Biopsychology Area This paper is going to consider the idea that electrical activity measures of the brain of a human being can be utilized as a great means for carrying out the study of the human memory.
• Biopsychology: Learning and Memory Relationship Memorization involves an integral function of the brain which is the storage of information. Memorization is directly linked to learning through the processes of encoding, storage, and retrieval of information.
• Apiculture: Memory in Honeybees They have a sharp memory to recall the previous locations of food, the scent, and the color where they can get the best nectar and pollen.
• Gender and Memory Capabilities of Humans However, in the spatial memory, none of the genders outdid the other and this questioned the prevalent idea that men are more advanced in spatial memory as compared to women.
• Collective Memory as “Time Out”: Repairing the Time-Community Link The essay will first give an account of how time helps to shape a community, various events that have been formulated in order to keep the community together and the effectiveness of these events in […]
• Community Gatherings and Collective Memory The objective of this paper is to examine some of the gatherings that take place in the community and how these gatherings are related to time.
• “The Memory Palace of Matteo Ricci” by Jonathan D. Spence: Concept of Memory Palaces The information concerning Matteo Ricci’s concept of memory palaces presented in the book is generalized to the extent that it is necessary to search for an explanation and some clarifications in the additional sources; “His […]
• Memory in Learning and Elapsed Time Manipulation And the longer they are subjected to presentation of stimuli, similar to a longer rehearsal, the better the learning rate. And that rats could communicate the flavor “learned”.
• Gender Factor Affecting Memory: Critically Evaluating of Researches In the book, ‘Gender and Memory,’ the authors, Leydesdorf, Passerini, and Thompson, point out that there is a significant difference in memories for narrative speech between men and women.
• Biologically Programmed Memory The brain, which carries the memory of the species, is a complex and delicate organ believed to carry the functions of the species.
• Sleep Patterns and Memory Performance of Children The article presents the essence, the methods and the results of the experiment which had to show the influence of TV and computer games on German children’s sleep.
• Psychology: Memory, Thinking, and Intelligence Information which serves as the stimuli moves from the sensory memory to the short term memory and finally to the long term memory for permanent storage.
• Working With Working Memory Even if we can only make a connection of something we see with a sound, it is easier to remember something we can speak, because the auditory memory helps the visual memory.
• Operant Conditioning, Memory Cue and Perception Operant conditioning through the use of punishment can be used to prevent or decrease a certain negative behavior, for example, when a child is told that he/she will lose some privileges in case he/she misbehaves, […]
• Human Memory: Serial Learning Experiment The background of the current research was stated in Ebbinghaus’ psychological study, and reveals the fact, that if e series of accidental symbols is offered for memorizing, the human memory will be able to memorize […]
• Hot and Cold Social Cognitions and Memory What is mentioned in biology text books and journals about the human brain is so small and almost insignificant compared to the myriad functions and parts of the brain that are yet to be explored.
• Memory Consolidation and Reconsolidation After Sleep The memory consolidation of the visual skill tasks is related to the REM sleep and the short wave component of the NREM.
• Attention, Perception and Memory Disorders Analysis Teenage is the time for experimentation, with a desire to be independent and try new and forbidden things like drugs or indulge in indiscrete sexual activity.
• Memory in Context of Optimal Studying Skill The focal point of the paper is to understand the different aspects of memory and find out the best method of studying.
• Autobiographical Memory and Cognitive Development During this stage important cognitive processes take place and are fundamental towards the development of autobiographical memory in the infants. This help the infants to have important memory cues that form part of the autobiographical […]
• Sensory and Motor Processes, Learning and Memory There are three processes involved in the sensory function of the eyes: the mechanical process, the chemical process, and the electrical process. The mechanical process starts as the stimuli passes through the cornea and […]
• Repressed Memory and Developing Teaching Strategies The author aims to emphasize the “importance, relevance, and potential to inform the lay public as well as our future attorneys, law enforcement officers, therapists, and current or future patients of therapists” with regards to […]
• Chauri Chaura Incident in History and Memory The book’s first half was a reconstruction, a narrative in historical view of the burning of the chowki or station and the account of the trial that focused on the testimony of the principal prosecution […]
• Hippocampus: Learning and Memory The limbic cortex, amygdala, and hippocampus are considered the processing parts of the limbic system while the output part comprises the septal nuclei and the hypothalamus.
• The Implications of False Memory and Memory Distortion The former refers to the manner of impressing into our minds the memories which we have acquired while the former refers to the manner by which a person reclaims the memories which have been stored […]
• Memory Comprehension Issue Review To sum up, studying with the background of loud music is counterproductive, as it is also an information channel that interferes with the comprehension and memorization of more important information.
• Memory Loss Treatment in Nursing Practice The identification of clinical manifestations of the disease is an important first step toward a correct diagnosis and the development of a plan of action to improve the patient’s short-term and long-term stability.
• The Interaction of Music and Memory Therefore, the research is of enormous significance for the understanding of individual differences in the connection between memory and music. Therefore, the research contributes to the understanding of the interaction of age with music and […]
• The Effect of Memory, Intelligence and Personality on Employee Performance and Behaviour The present paper will seek to explain the theoretical background on memory, intelligence and personality and evaluate the influence of these factors on work performance and employee behaviours.
• Cogmed Working Memory Training in Children The methodology of the study is strong, and the number of participants is adequate to measure the effects of the program.
• Elderly Dementia: Holistic Approaches to Memory Care The CMAI is a nursing-rated questionnaire that evaluates the recurrence of agitation in residents with dementia. Since the research focuses on agitation, the CMAI was utilized to evaluate the occurrence of agitation at baseline.
• The Conceptual Relationship Between Memory and Imagination In particular, the scholar draws parallels between these processes by addressing the recorded activity of specific brain structures when “remembering the past and imagining the future”.
• “How Reliable Is Your Memory?” by Elizabeth Loftus Regardless of how disturbing and sorrowful it may be, and even when pointed out that this certain memory is false, a person may be unable to let it go.
• Cognitive Psychology: Memory and Interferences For instance, I remember how to organize words in the right way to form a sentence and I know the capitals of countries.
• Memory as a Topic of Modern Studies in Psychology Holt and Delvenne present a research paper on the effect of rehearsing on memorization, stating that there is a connection between “spatial” attention, repetition, and short-term memory.
• How Memory and Intelligence Change as We Age The central argument of the paper is that intelligence and memory change considerably across the lifespan, but these alterations are different in the two concepts. The article by Ofen and Shing is a valuable contribution […]
• Memory Acquisition and Information Processing The problem of disagreeing with memories can be explained by a closer look at the process of memory acquisition. Most part of the sensory information is not encoded due to selective attention.
• Memory and Motivation at History Lesson Step 1: Presentation uncovering the unknown facts about the famous people Step 2: Identifying the inaccuracies in groups Step 3: Discussion of the results Step 4: in-class quiz on the presented material Step 5: working […]
• Varlam Shalamov on Memory and Psychological Resilience The soldiers sent to therapists such as Rivers and Yealland in Regeneration had one problem in common they were unable to forget the traumatic and frightening experiences that had affected them in the past.
• Learning Activity and Memory Improvement The easiest way to explain the difference between implicit and explicit types of learning is to think of the latter as active learning and of the former – as passive one.
• Surrealism and Dali’s “The Persistence of Memory” Of course, The Persistence of Memory is one of the best-known works, which is often regarded as one of the most conspicuous illustrations of the movement.
• Psychology: Short-Term and Working Memory The thing is that the term short-term memory is used to describe the capacity of the mind to hold a small piece of information within a very short period, approximately 20 seconds.
• Dealing With the Limitations of Flash Memory Implanted medical chip technology can help to reduce the amount of medical misdiagnosis that occur in hospitals and can also address the issue of the amount of money that Jones Corp.pays out to its clients […]
• Free and Serial Memory Recalls in Experiments In the study, the experimenters changed the order in which the items were presented to the participants before each trial to test the ability of the subject to recognize these words it was observed that […]
• Learning Disabilities and Memory Disorders Large amounts of phenylalanine in the blood will result in complications of the neurons in the central nervous system referred to as myelinization of the cerebral hemispheres.
• Collective Memory and Patriotic Myth in American History However, to think that colonists and early Americans pursued a general policy of killing or driving out the native Indians is incorrect.
• When the Desire Is Not Enough: Flash Memory As a result, a number of rather uncomfortable proposals were made to the founders of Flash, but the company’s members had to accept certain offers for the financing to continue and the firm not to […]
• Effects of Marijuana on Memory of Long-Term Users The pivotal aim of the proposed study is to evaluate the impact of marijuana use on long-term memory of respondents. The adverse impact of marijuana after the abstinent syndrome refers to significant changes in prefrontal […]
• Amphetamines and Their Effects on Memory The scope of the problem of stimulant abuse is quite important in nowadays medicine since the application of amphetamine is not explored in an in-depth manner.
• Memory Retrieval, Related Processes and Secrets The resulting impression of having experienced what is portrayed in the picture leads to the creation of false memories. The authors of the study make it clear that placing one in specific visual and spatial […]
• Mnemonics for Memory Improvement in Students The selected participants will be split into two groups that will be asked to memorize a set of words from a story with the help of the suggested technique.
• Sociocultural Memory in European and Asian Americans The Asian perspective on the use of memory, however, suggests that a much greater emphasis should be placed on using memory as a learning resource so that it can be expanded with the help of […]
• Emotional Memory: Negative and Positive Experiences For instance, autobiographical memory provides a chance to remember the events that shaped one’s personality and defined the further course of one’s development.
• Concreteness of Words and Free Recall Memory The study hypothesized that the free recall mean of concrete words is not statistically significantly higher than that of abstract words.
• The Public Memory of the Holocaust In addition to his pain, Levi concerns the increasing temporal distance and habitual indifference of hundreds of millions of people towards the Holocaust and the survivors1 It causes the feeling of anxiety that was fuelled […]
• Memory Formation and Maintenance The first similarity between working memory and long term memory is that in both cases, tasks retrieve information from secondary memory, although sometimes working memory tasks retrieve information from the primary memory. After completion of […]
• Working Memory Training and Its Controversies As a result, a range of myths about WM has been addressed and subverted successfully, including the one stating that WM related training cannot be used to improve one’s intellectual abilities and skills.
• Music and Human Memory Connection The effects of music on people vary considerably, and this project should help to understand the peculiar features of the connection between human memory and music.
• Police Shooting Behaviour, Memory, and Emotions The subject of the study was limited to analyzing the shooting behavior of police officers in danger-related situations. It is supposed that officers with low capacity of working memory are more likely to shoot the […]
• Place-Based Memory Studies and Thinking Architecture There is a need to inform the society of the history represented by the sites and educate the masses on events leading to such occurrences.
• Working Memory Training: Benefits and Biases The research results indicate that the effects of stereotyping on the development of WM and the relevant skills are direct and rather drastic.
• Biopsychology of Learning and Memory The hippocampus is a brain region in the form of a horseshoe that plays an essential role in the transformation of information from the short-term memory to the long-term memory.
• Working Memory Concept The central executive, as the name implies, is the primary component of the working memory system; every other component is subservient to it.
• False Memory and Emotions Experiment The hypothesis was as follows: a list of associate words creates a false memory by remembering a critical lure when the list is presented to a subject and a recall test done shortly after that.
• Building of Memory: Managing Creativity Through Action It could be important for the team to understand Kornfield’s vision of the project, the main and secondary tasks, the project timeline, and the general outline of it. The third technique is to ensure face-to-face […]
• Misinformation Effect and Memory Impairment It is important to determine the science behind the misinformation effect, because the implication of the study goes beyond the confines of psychology.
• Memory Distortions Develop Over Time Memory is the ability to recall what happened in the past or the process through which one’s brain stores events and reproduce them in the future. Simpson were put on a scoreboard to analyze the […]
• Working Memory Load and Problem Solving The present research focuses on the way working memory load affects problem solving ability and the impact working memory capacity has on problem solving ability of people.
• Sensory Memory Duration and Stimulus Perception Cognitive psychologists argue that perceived information takes one second in the sensory memory, one minute in the short-term memory and a life-time in the long-term memory.
• Memory Study: Mnemonics Techniques Having carried out two experiments, Oberauer comes to the conclusion that information in working memory is highly organized and has its own structure and understanding of this structure can help to improve the work of […]
• Memory Study: Different Perspectives Having carried out two experiments, Oberauer comes to the conclusion that information in working memory is highly organized and has its own structure and understanding of this structure can help to improve the work of […]
• Individual Recognition Decisions and Memory Strength Signal The individual recognition decision and the memory strength will be compared to determine their relation. A positive correlation between the individual recognition decisions and the aggregated memory strength will be shown.
• Working Memory Concept: Psychological Views To begin with, the findings support the use of the Working-Memory Model because it offers a clear distinction between the subordinate memory systems and the “central executive” memory.
• Memory Strategies and Their Effects on the Body Memory problems are a common concern in the society due to the increased rate of memory problems among the individuals. This is a strategy that uses chemicals to suppress the adverse effects of memory problems.
• George Santayana’s Philosophy Views on Historical Memory To Plato, democracy was the worst form of governance because it was the tyranny of the multitude. Furthermore, the effects of the war were hard to take because people lost everything they had.
• Cognitive Stimulation on Patients With Impaired Memory Cognitive stimulation therapy is effective in mitigating the effects of dementia. As a result, the researchers tested cognitive stimulation therapy in clinical trials.
• Memory and Emotions in Personal Experience I tried to convince Sherry that the kind of life she led will not do good to her. I thought that Sherry is a grown-up person who would understand the mistakes she had done and […]
• Face Recognition and Memory Retention It is imperative to mention that cognitive process is very significant in face recognition especially due to its role in storage and retrieval of information from long-term memory.
• False Memory Condition: Experimental Studies It is therefore important to conduct some experiments to see the differences between the correct memory and the false memory. The distracters and words to be identified were the variables that were independent.
• Memory Capacity and Age Correlation Since young adults have high levels of positive emotions and low levels of negative emotions, the positive emotions enable them to enhance their memory capacity for positive information.
• Conflict at Walt Disney Company: A Distant Memory? The conflict between Michael Eisner and the Weinstein brothers, the two board members, and Steve Jobs was related to a dysfunctional form of conflict.
• Eye-Path and Memory-Prediction Framework Online marketing and advertising actively develop nowadays, and modern advertisers need to focus on the customers’ attitudes and behaviours in the context of the effectiveness of the advertisement’s location on the web page.
• Long Term Memory and Retrieval The mode of presenting the items in sequence in the first presentation has great impact on the results and validity of the study.
• Denying the Holocaust: The Growing Assault on Truth and Memory by Deborah Lipstadt The book is divided into chapters that focus on the history and methods that are used to distort the truth and the memory of the Holocaust.
• Power, Memory and Spectacle on Saddam Hussein’s Death His rational was that the only way to unite the country was to eliminate the elements of division who in his opinion were the opposition.
• Theoretical Models in Understanding Working Memory The second model for understanding the processes involved in working memory is the Baddeley and Hitch multi-component model which states that working memory operates via a system of “slave systems” and a central controller which […]
• Semantic Memory and Language Production
• Basic Functions of Memory and Language
• The Concept of Autobiographical Memory
• Neuroimaging Experiments and Memory Loss Studies
• Semantic Memory and Language Production Relationship
• Chinese Novellas: The Role of Memory and Perception
• Memory Lane and Morality
• Autonoetic Consciousness in Autobiographical Memory
• “Memory by Analogy” Film Concepts
• Film About Hirosima Memory by Analogy
• Ecstasy and Memory Impairment Neurological Correlation
• Memory Theories in Developing Marketing Strategies of the iPad
• Definition of Storage Locations in Memory
• Establishing False Memory in Humans
• Constructive Nature of Memory
• Comparison and Contrast Assignment on “Paradoxical Effects of Presentation Modality on False Memory,” Article and “Individual Differences in Learning and Remembering Music.”
• How to Improve Your Memory
• Memory Systems of the Brain
• Strategies of the Memory
• Brain and Memory
• Biology of Memory: Origins and Structures
• Amnesia and Long-Term Memory
• Cannabis and Its Effects on Long Term Memory
• Mental Chronometry: Response Time and Accuracy
• Working Memory in Attention Deficit and Hyperactivity Disorder (ADHD)
• False Memory Syndrome: Is It Real?
• The Relationships of Working Memory, Secondary Memory, and General Fluid Intelligence: Working Memory Is Special
• Memory Process: Visual Receptivity and Retentiveness
• How Age and Diseases Affect Memory
• Memory, Thinking, and Intelligence
• Language and Memory Paper
• Memory: Understanding Consciousness
• Sleep Improves Memory
• Language Rules for a Reliable Semantic Memory
• Chicago (A-D)
• Chicago (N-B)

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Working memory articles from across Nature Portfolio

Working memory is the active and robust retention of multiple bits of information over the time-scale of a few seconds. It is distinguished from short-term memory by the involvement of executive or attentional control that makes the information flexible yet resistant to interference.

Latest Research and Reviews

Alpha neurofeedback training improves visual working memory in healthy individuals

• Wenbin Zhou

Control of working memory by phase–amplitude coupling of human hippocampal neurons

Hippocampal theta–gamma phase–amplitude coupling integrates cognitive control and working memory storage across brain areas in humans.

• Jonathan Daume
• Jan Kamiński
• Ueli Rutishauser

Evidence for a competitive relationship between executive functions and statistical learning

• Felipe Pedraza
• Bence C. Farkas
• Dezső Németh

Chronic phase advances reduces recognition memory and increases vascular cognitive dementia-like impairments in aged mice

• Jennifer A. Liu
• Jacob R. Bumgarner
• Randy J. Nelson

Be prepared for interruptions: EEG correlates of anticipation when dealing with task interruptions and the role of aging

• Stephan Getzmann
• Daniel Schneider

Self-reported strategy use in working memory tasks

• Liisa Ritakallio
• Daniel Fellman
• Matti Laine

News and Comment

Reply to ‘efficiency and capacity mechanisms can coexist in cognitive training’.

• Claudia C. von Bastian
• Sylvie Belleville
• Tilo Strobach

Efficiency and capacity mechanisms can coexist in cognitive training

• Da-Wei Zhang
• Bruno Sauce

Labels aid visual working memory

• Teresa Schubert

A frequency location to remember

Neuromodulation with specific frequencies at specific brain locations selectively enhances either working memory or long-term memory in older adult humans.

• Jake Rogers

Revisiting mixture models of memory

Probabilistic mixture models have contributed significantly to advancements in visual working memory research in recent decades. In a new paper, Schurgin and colleagues revisit the basic assumptions of mixture models and suggest that we cannot understand memory without first considering perception.

• Blaire Dube
• Julie D. Golomb

Random connections in memory

• Anne-Marike Schiffer

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Workings of working memory detailed

Investigators identify a group of cells that help coordinate the brain's focus and storage functions for short-term information retention.

Cedars-Sinai investigators have discovered how brain cells responsible for working memory -- the type required to remember a phone number long enough to dial it -- coordinate intentional focus and short-term storage of information.

The study detailing their discovery was published in the peer-reviewed journal Nature .

"We have identified for the first time a group of neurons, influenced by two types of brain waves, that coordinate cognitive control and the storage of sensory information in working memory," said Jonathan Daume, PhD, a postdoctoral scholar in the Rutishauser Lab at Cedars-Sinai and first author of the study. "These neurons don't contain or store information, but are crucial to the storage of short-term memories."

Working memory, which requires the brain to store information for only seconds, is fragile and requires continued focus to be maintained, said Ueli Rutishauser, PhD, director of the Center for Neural Science and Medicine at Cedars-Sinai and senior author of the study. It can be affected by different diseases and conditions.

"In disorders such as Alzheimer's disease or attention-deficit hyperactivity disorder, it is often not memory storage, but rather the ability to focus on and retain a memory once it is formed that is the problem," said Rutishauser, who is a professor of Neurosurgery, Neurology and Biomedical Sciences at Cedars-Sinai. "We believe that understanding the control aspect of working memory will be fundamental for developing new treatments for these and other neurological conditions."

To explore how working memory functions, investigators recorded the brain activity of 36 hospitalized patients who had electrodes surgically implanted in their brains as part of a procedure to diagnose epilepsy. The team recorded the activity of individual brain cells and brain waves while the patients performed a task that required use of working memory.

On a computer screen, patients were shown either a single photo or a series of three photos of various people, animals, objects or landscapes. Next, the screen went blank for just under three seconds, requiring patients to remember the photos they just saw. They were then shown another photo and asked to decide whether it was the one (or one of the three) they had seen before.

When patients performing the working memory task were able to respond quickly and accurately, investigators noted the firing of two groups of neurons: "category" neurons that fire in response to one of the categories shown in the photos, such as animals, and "phase-amplitude coupling," or PAC, neurons.

PAC neurons, newly identified in this study, don't hold any content, but use a process called phase-amplitude coupling to ensure the category neurons focus and store the content they have acquired. PAC neurons fire in time with the brain's theta waves, which are associated with focus and control, as well as to gamma waves, which are linked to information processing. This allows them to coordinate their activity with category neurons, which also fire in time to the brain's gamma waves, enhancing patients' ability to recall information stored in working memory.

"Imagine when the patient sees a photo of a dog, their category neurons start firing 'dog, dog, dog' while the PAC neurons are firing 'focus/remember,'" Rutishauser said. "Through phase-amplitude coupling, the two groups of neurons create a harmony superimposing their messages, resulting in 'remember dog.' It is a situation where the whole is greater than the sum of its parts, like hearing the musicians in an orchestra play together. The conductor, much like the PAC neurons, coordinates the various players to act in harmony."

PAC neurons do this work in the hippocampus, a part of the brain that has long been known to be important for long-term memory. This study offers the first confirmation that the hippocampus also plays a role in controlling working memory, Rutishauser said.

This study was conducted as part of a multi-institutional consortium funded by the National Institutes of Health's Brain Research Through Advancing Innovative Neurotechnologies Initiative, or The BRAIN Initiative, and led by Cedars-Sinai. The data in this study is pooled across Cedars-Sinai, the University of Toronto, and the Johns Hopkins School of Medicine, resulting in a statistically powerful study that a single institution could not accumulate on its own given the difficulty of these experiments.

"One of the aims of the BRAIN Initiative is to uncover -- through the use of innovative technologies -- properties of the human brain that have so far been difficult, if not impossible, to study" said Dr. John Ngai, PhD, director of the NIH BRAIN Initiative. "Here, by leveraging unusual opportunities supported by the initiative to illuminate complex processes in humans, the Rutishauser Lab is shedding light on the way certain neurons support how memories are stored in the brain -- a process that is far from understood in devastating brain disorders such as Alzheimer's disease and other dementias."

• Nervous System
• Brain Tumor
• Psychology Research
• Birth Defects
• Neuroscience
• Intelligence
• Brain Injury
• Mental confusion
• Brain damage
• Memory bias
• Pernicious anemia
• Brain tumor

Story Source:

Materials provided by Cedars-Sinai Medical Center . Note: Content may be edited for style and length.

Journal Reference :

• Jonathan Daume, Jan Kamiński, Andrea G. P. Schjetnan, Yousef Salimpour, Umais Khan, Michael Kyzar, Chrystal M. Reed, William S. Anderson, Taufik A. Valiante, Adam N. Mamelak, Ueli Rutishauser. Control of working memory by phase–amplitude coupling of human hippocampal neurons . Nature , 2024; DOI: 10.1038/s41586-024-07309-z

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April 17, 2024

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Does using your brain more at work help ward off thinking, memory problems?

by American Academy of Neurology

The harder your brain works at your job, the less likely you may be to have memory and thinking problems later in life, according to a new study published in the journal Neurology . This study does not prove that stimulating work prevents mild cognitive impairment. It only shows an association.

"We examined the demands of various jobs and found that cognitive stimulation at work during different stages in life—during your 30s, 40s, 50s and 60s—was linked to a reduced risk of mild cognitive impairment after the age of 70," said study author Trine Holt Edwin, MD, Ph.D., of Oslo University Hospital in Norway. "Our findings highlight the value of having a job that requires more complex thinking as a way to possibly maintain memory and thinking in old age."

The study looked at 7,000 people and 305 occupations in Norway.

Researchers measured the degree of cognitive stimulation that participants experienced while on the job. They measured the degree of routine manual, routine cognitive, non-routine analytical, and non-routine interpersonal tasks, which are skill sets that different jobs demand.

Routine manual tasks demand speed, control over equipment, and often involve repetitive motions, typical of factory work. Routine cognitive tasks demand precision and accuracy of repetitive tasks, such as in bookkeeping and filing.

Non-routine analytical tasks refer to activities that involve analyzing information, engaging in creative thinking and interpreting information for others. Non-routine interpersonal tasks refer to establishing and maintaining personal relationships , motivating others and coaching. Non-routine cognitive jobs include public relations and computer programming.

Researchers divided participants into four groups based on the degree of cognitive stimulation that they experienced in their jobs.

The most common job for the group with the highest cognitive demands was teaching. The most common jobs for the group with the lowest cognitive demands were mail carriers and custodians.

After age 70, participants completed memory and thinking tests to assess whether they had mild cognitive impairment. Of those with the lowest cognitive demands, 42% were diagnosed with mild cognitive impairment. Of those with the highest cognitive demands, 27% were diagnosed with mild cognitive impairment.

After adjustment for age, sex, education, income and lifestyle factors, the group with the lowest cognitive demands at work had a 66% higher risk of mild cognitive impairment compared to the group with the highest cognitive demands at work.

"These results indicate that both education and doing work that challenges your brain during your career play a crucial role in lowering the risk of cognitive impairment later in life," Edwin said. "Further research is required to pinpoint the specific cognitively challenging occupational tasks that are most beneficial for maintaining thinking and memory skills."

A limitation of the study was that even within identical job titles, individuals might perform different tasks and experience different cognitive demands.

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