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Working Memory From the Psychological and Neurosciences Perspectives: A Review

Wen jia chai, aini ismafairus abd hamid, jafri malin abdullah.

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Edited by: Gesualdo M. Zucco, Università degli Studi di Padova, Italy

Reviewed by: Erika Borella, Università degli Studi di Padova, Italy; Emily M. Elliott, Louisiana State University, United States

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

This article was submitted to Cognitive Science, a section of the journal Frontiers in Psychology

Received 2017 Nov 24; Accepted 2018 Mar 9; Collection date 2018.

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.

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.

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

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.

Working memory (WM) studies in the healthy brain.

AUT, Alternate Uses Task; fMRI, Functional magnetic resonance imaging; TMS, Transcranial magnetic stimulation; SMA, Supplementary motor area; IFG, Inferior frontal gyrus; FEF, Frontal eye field; AI, Anterior insula; IFJ, Inferior frontal junctions; DLPFC, Dorsolateral prefrontal cortex; MD, Mediodorsal thalamus; PFC, Prefrontal cortex; ACC, Anterior cingulate cortex; PPC, Posterior parietal cortex; MTL, Medial temporal lobe; MFG, Middle frontal gyrus; FPC, Frontopolar cortex; PCS, Precentral sulcus; IPS, Intraparietal sulcus; IFS, Inferior frontal sulcus; SN/VTA, Substantia nigra and ventral tegmental area; STG, Superior temporal gyrus; VLPFC, Ventrolateral prefrontal cortex; OFC, Orbitofrontal cortex.

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.

Working memory (WM) studies in relation to age.

APPsw, Amyloid precursor protein, Swedish; CWMS, Categorization working memory span; LST, Listening span test; WAIS, Wechsler Adult Intelligence Scale; fMRI, Functional magnetic resonance imaging; MRI, Magnetic resonance imaging; ROIs, Regions of interest.

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 ).

Working memory (WM) studies in the diseased brain.

ADHD, Attention deficit/hyperactivity disorder; MDD, Major depressive disorder; WMTB-C, Working memory test battery for children; VSAT, Visual Serial Addition Task; AWMA, Automated Working Memory Assessment; fMRI, Functional magnetic resonance imaging; ROIs, Regions of interest.

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 .

Working memory (WM) studies in the TBI group.

MDD, Major depressive disorder; CLS, Category listening span task; CLS-DT, Category listening span dual-task; VSS, Visuospatial span task; VSS-DT, Visuospatial span dual-task; WISC-III, Wechsler Intelligence Scale for Children-Third Edition-Australian Adaptation; TOL, Tower of London; COWAT, Controlled Oral Word Association Test; EEG, Electroencephalography; fMRI, Functional magnetic resonance imaging; ERP, Event-related potential; DTI, Diffusor tensor imaging; fNIRS, Functional near-infrared spectroscopy; ROIs, Regions of interest.

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.

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.

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

• Andersen R. A., Cui H. (2009). Review intention, action planning, and decision making in parietal-frontal circuits. Neuron 63 568–583. 10.1016/j.neuron.2009.08.028 [ DOI ] [ PubMed ] [ Google Scholar ]
• Anderson V., Catroppa C. (2007). Memory outcome at 5 years post-childhood traumatic brain injury. Brain Inj. 21 1399–1409. 10.1080/02699050701785070 [ DOI ] [ PubMed ] [ Google Scholar ]
• Arendash G. W., Schleif W., Rezai-Zadeh K., Jackson E. K., Zacharia L. C., Cracchiolo J. R., et al. (2006). Caffeine protects Alzheimer’s mice against cognitive impairment and reduces brain β-amyloid production. Neuroscience 142 941–952. 10.1016/j.neuroscience.2006.07.021 [ DOI ] [ PubMed ] [ Google Scholar ]
• Ashkenazi S., Rosenberg-lee M., Metcalfe A. W. S., Swigart A. G., Menon V. (2013). Neuropsychologia visuo – spatial working memory is an important source of domain-general vulnerability in the development of arithmetic cognition. Neuropsychologia 51 2305–2317. 10.1016/j.neuropsychologia.2013.06.031 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Baars B. J., Franklin S. (2003). How conscious experience and working memory interact. Trends Cogn. Sci. 7 166–172. 10.1016/S1364-6613(03)00056-1 [ DOI ] [ PubMed ] [ Google Scholar ]
• Baddeley A. (1996). Exploring the central executive. Q. J. Exp. Psychol. A 49 5–28. 10.1080/713755608 [ DOI ] [ Google Scholar ]
• Baddeley A. (2010). Working memory. Curr. Biol. 20 R136–R140. 10.1016/j.cub.2009.12.014 [ DOI ] [ PubMed ] [ Google Scholar ]
• Baddeley A. (2012). Working memory: theories, models, and controversies. Annu. Rev. Psychol. 63 1–29. 10.1146/annurev-psych-120710-100422 [ DOI ] [ PubMed ] [ Google Scholar ]
• Baddeley A., Hitch G. (1974). Working memory. Psychol. Learn. Motiv. 8 47–89. 10.1016/j.cub.2009.12.014 [ DOI ] [ PubMed ] [ Google Scholar ]
• Baddeley A. D. (2000a). The episodic buffer : a new component of working memory? Trends Cogn. Sci. 4 417–423. 10.1016/S1364-6613(00)01538-2 [ DOI ] [ PubMed ] [ Google Scholar ]
• Baddeley A. D. (2000b). Short-Term and Working Memory. The Oxford Handbook of Memory. Oxford: Oxford University Press. [ Google Scholar ]
• Bailey N. W., Rogasch N. C., Hoy K. E., Maller J. J., Segrave R. A., Sullivan C. M., et al. (2017). Increased gamma connectivity during working memory retention following traumatic brain injury. Brain Inj. 31 379–389. 10.1080/02699052.2016.1239273 [ DOI ] [ PubMed ] [ Google Scholar ]
• Balderston N. L., Vytal K. E., O’Connell K., Torrisi S., Letkiewicz A., Ernst M., et al. (2017). Anxiety patients show reduced working memory related dlPFC activation during safety and threat. Depress. Anxiety 34 25–36. 10.1002/da.22518 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Barrouillet P., Bernardin S., Camos V. (2004). Time constraints and resource sharing in adults’ working memory spans. J. Exp. Psychol. Gen. 133 83–100. 10.1037/0096-3445.133.1.83 [ DOI ] [ PubMed ] [ Google Scholar ]
• Barrouillet P., Camos V. (2007). “The time-based resource-sharing model of working memory,” in The Cognitive Neuroscience of Working Memory ed. Osaka N. (Oxford: Oxford University Press; ) 59–80. 10.1093/acprof:oso/9780198570394.003.0004 [ DOI ] [ Google Scholar ]
• Barrouillet P., Gavens N., Vergauwe E., Gaillard V., Camos V. (2009). Working memory span development: a time-based resource-sharing model account. Dev. Psychol. 45 477–490. 10.1037/a0014615 [ DOI ] [ PubMed ] [ Google Scholar ]
• Bolkan S. S., Stujenske J. M., Parnaudeau S., Spellman T. J., Rauffenbart C., Abbas A. I., et al. (2017). Thalamic projections sustain prefrontal activity during working memory maintenance. Nat. Neurosci. 20 987–996. 10.1038/nn.4568 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Borella E., Carretti B., Sciore R., Capotosto E., Taconnat L., Cornoldi C., et al. (2017). Training working memory in older adults: is there an advantage of using strategies? Psychol. Aging 32 178–191. 10.1037/pag0000155 [ DOI ] [ PubMed ] [ Google Scholar ]
• Chein J. M., Moore A. B., Conway A. R. A. (2011). NeuroImage domain-general mechanisms of complex working memory span. Neuroimage 54 550–559. 10.1016/j.neuroimage.2010.07.067 [ DOI ] [ PubMed ] [ Google Scholar ]
• Chen C. J., Wu C. H., Liao Y. P., Hsu H. L., Tseng Y. C., Liu H. L., et al. (2012). Working memory in patients with mild traumatic brain injury: functional MR imaging analysis. Radiology 264 844–851. 10.1148/radiol.12112154 [ DOI ] [ PubMed ] [ Google Scholar ]
• Cowan N. (1999). “An embedded-processes model of working memory,” in Models of Working Memory: Mechanisms of Active Maintenance and Executive Control eds Miyake A., Shah P. (Cambridge: Cambridge University Press; ). 10.1017/S0140525X01003922 [ DOI ] [ Google Scholar ]
• Cowan N. (2005). Working memory capacity. Exp. Psychol. 54 245–246. 10.1027/1618-3169.54.3.245 [ DOI ] [ Google Scholar ]
• Cowan N. (2008). What are the differences between long-term, short-term, and working memory? Prog. Brain Res. 169 323–338. 10.1016/S0079-6123(07)00020-9 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Cowan N. (2010). The magical mystery four. Curr. Dir. Psychol. Sci. 19 51–57. 10.1177/0963721409359277 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Dahlin E., Nyberg L., Bäckman L., Neely A. S. (2008). Plasticity of executive functioning in young and older adults: immediate training gains, transfer, and long-term maintenance. Psychol. Aging 23 720–730. 10.1037/a0014296 [ DOI ] [ PubMed ] [ Google Scholar ]
• Daneman M., Carpenter P. A. (1980). Individual differences in working memory and reading. J. Verbal Learn. Verbal Behav. 19 450–466. 10.1016/S0022-5371(80)90312-6 [ DOI ] [ Google Scholar ]
• D’Esposito M., Postle B. R. (2015). The cognitive neuroscience of working memory. Annu. Rev. Psychol. 66 115–142. 10.1146/annurev-psych-010814-015031 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Dikmen S. S., Corrigan J. D., Levin H. S., Machamer J., Stiers W., Weisskopf M. G. (2009). Cognitive outcome following traumatic brain injury. J. Head Trauma Rehabil. 24 430–438. 10.1097/HTR.0b013e3181c133e9 [ DOI ] [ PubMed ] [ Google Scholar ]
• Dima D., Jogia J., Frangou S. (2014). Dynamic causal modeling of load-dependent modulation of effective connectivity within the verbal working memory network. Hum. Brain Mapp. 35 3025–3035. 10.1002/hbm.22382 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Dobryakova E., Boukrina O., Wylie G. R. (2015). Investigation of information flow during a novel working memory task in individuals with traumatic brain injury. Brain Connect. 5 433–441. 10.1089/brain.2014.0283 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Duff S. J., Hampson E. (2000). A beneficial effect of estrogen on working memory in postmenopausal women taking hormone replacement therapy. Horm. Behav. 38 262–276. 10.1006/hbeh.2000.1625 [ DOI ] [ PubMed ] [ Google Scholar ]
• Dunning D. L., Westgate B., Adlam A.-L. R. (2016). A meta-analysis of working memory impairments in survivors of moderate-to-severe traumatic brain injury. Neuropsychology 30 811–819. 10.1037/neu0000285 [ DOI ] [ PubMed ] [ Google Scholar ]
• Ellis M. U., DeBoard Marion S., McArthur D. L., Babikian T., Giza C., Kernan C. L., et al. (2016). The UCLA study of children with moderate-to-severe traumatic brain injury: event-related potential measure of interhemispheric transfer time. J. Neurotrauma 33 990–996. 10.1089/neu.2015.4023 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Engle R. W. (2002). Working memory capacity as executive attention. Curr. Dir. Psychol. Sci. 11 19–23. 10.1111/1467-8721.00160 [ DOI ] [ Google Scholar ]
• Engle R. W., Kane M. J. (2004). “Executive attention, working memory capacity, and a two-factor theory of cognitive control,” in The Psychology of Learning and Motivation: Advances in Research and Theory ed. Ross B. H. (New York, NY: Elsevier; ) 145–199. 10.1016/S0079-7421(03)44005-X [ DOI ] [ Google Scholar ]
• Farrer T. J. (2017). Encyclopedia of Geropsychology ed. Pachana N. A. Singapore: Springer; 10.1007/978-981-287-080-3 [ DOI ] [ Google Scholar ]
• Fiebig F., Lansner A. (2017). A spiking working memory model based on hebbian short-term potentiation. J. Neurosci. 37 83–96. 10.1523/JNEUROSCI.1989-16.2016 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Friston K., Moran R., Seth A. K. (2013). Analysing connectivity with granger causality and dynamic causal modelling. Curr. Opin. Neurobiol. 23 172–178. 10.1016/j.conb.2012.11.010 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Gorman S., Barnes M. A., Swank P. R., Prasad M., Cox C. S., Ewing-Cobbs L. (2016). Does processing speed mediate the effect of pediatric traumatic brain injury on working memory? Neuropsychology 30 263–273. 10.1037/neu0000214 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Gorman S., Barnes M. A., Swank P. R., Prasad M., Ewing-Cobbs L. (2012). The effects of pediatric traumatic brain injury on verbal and visual-spatial working memory. J. Int. Neuropsychol. Soc. 18 29–38. 10.1017/S1355617711001251 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Gottwald B., Wilde B., Mihajlovic Z., Mehdorn H. M. (2004). Evidence for distinct cognitive deficits after focal cerebellar lesions. J. Neurol. Neurosurg. Psychiatry 75 1524–1531. 10.1136/jnnp.2003.018093 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Grot S., Légaré V. P., Lipp O., Soulières I., Dolcos F., Luck D. (2017). Abnormal prefrontal and parietal activity linked to deficient active binding in working memory in schizophrenia. Schizophr. Res. 188 68–74. 10.1016/j.schres.2017.01.021 [ DOI ] [ PubMed ] [ Google Scholar ]
• Guye S., von Bastian C. C. (2017). Working memory training in older adults: bayesian evidence supporting the absence of transfer. Psychol. Aging 32 732–746. 10.1037/pag0000206 [ DOI ] [ PubMed ] [ Google Scholar ]
• Haller S., Montandon M.-L., Rodriguez C., Moser D., Toma S., Hofmeister J., et al. (2017). Caffeine impact on working memory-related network activation patterns in early stages of cognitive decline. Neuroradiology 59 387–395. 10.1007/s00234-017-1803-5 [ DOI ] [ PubMed ] [ Google Scholar ]
• Haller S., Rodriguez C., Moser D., Toma S., Hofmeister J., Sinanaj I., et al. (2013). Acute caffeine administration impact on working memory-related brain activation and functional connectivity in the elderly: a BOLD and perfusion MRI study. Neuroscience 250 364–371. 10.1016/j.neuroscience.2013.07.021 [ DOI ] [ PubMed ] [ Google Scholar ]
• Hedden T., Gabrieli J. D. E. (2004). Insights into the ageing mind: a view from cognitive neuroscience. Nat. Rev. Neurosci. 5 87–96. 10.1038/nrn1323 [ DOI ] [ PubMed ] [ Google Scholar ]
• Heinzel S., Rimpel J., Stelzel C., Rapp M. A. (2017). Transfer effects to a multimodal dual-task after working memory training and associated neural correlates in older adults – a pilot study. Front. Hum. Neurosci. 11:85. 10.3389/fnhum.2017.00085 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Hillary F. G., Medaglia J. D., Gates K., Molenaar P. C., Slocomb J., Peechatka A., et al. (2011). Examining working memory task acquisition in a disrupted neural network. Brain 134 1555–1570. 10.1093/brain/awr043 [ DOI ] [ PubMed ] [ Google Scholar ]
• Hsu H.-L., Chen D. Y.-T., Tseng Y.-C., Kuo Y.-S., Huang Y.-L., Chiu W.-T., et al. (2015). Sex differences in working memory after mild traumatic brain injury: a functional MR imaging study. Radiology 276 828–835. 10.1148/radiol.2015142549 [ DOI ] [ PubMed ] [ Google Scholar ]
• Humphreys M. S., Bain J. D., Pike R. (1989). Different ways to cue a coherent memory system: a theory for episodic, semantic, and procedural tasks. Psychol. Rev. 96 208–233. 10.1037/0033-295X.96.2.208 [ DOI ] [ Google Scholar ]
• Janowsky J. S., Chavez B., Orwoll E. (2000). Sex steroids modify working memory. J. Cogn. Neurosci. 12 407–414. 10.1162/089892900562228 [ DOI ] [ PubMed ] [ Google Scholar ]
• Jimura K., Chushak M. S., Westbrook A., Braver T. S. (2017). Intertemporal decision-making involves prefrontal control mechanisms associated with working memory. Cereb. Cortex 10.1093/cercor/bhx015 [Epub ahead of print]. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Joseph J. E., Swearingen J. E., Corbly C. R., Curry T. E., Kelly T. H. (2012). Influence of estradiol on functional brain organization for working memory. Neuroimage 59 2923–2931. 10.1016/j.neuroimage.2011.09.067 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Karbach J., Verhaeghen P. (2014). Making working memory work: a meta-analysis of executive control and working memory training in younger and older adults. Psychol. Sci. 25 2027–2037. 10.1177/0956797614548725 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Kim C., Kroger J. K., Calhoun V. D., Clark V. P. (2015). The role of the frontopolar cortex in manipulation of integrated information in working memory. Neurosci. Lett. 595 25–29. 10.1016/j.neulet.2015.03.044 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Klaassen E. B., De Groot R. H. M., Evers E. A. T., Snel J., Veerman E. C. I., Ligtenberg A. J. M., et al. (2013). The effect of caffeine on working memory load-related brain activation in middle-aged males. Neuropharmacology 64 160–167. 10.1016/j.neuropharm.2012.06.026 [ DOI ] [ PubMed ] [ Google Scholar ]
• Kovacs K., Conway A. R. A. (2016). Process overlap theory: a unified account of the general factor of intelligence. Psychol. Inq. 27 151–177. 10.1080/1047840X.2016.1153946 [ DOI ] [ Google Scholar ]
• Le T. M., Borghi J. A., Kujawa A. J., Klein D. N., Leung H.-C. (2017). Alterations in visual cortical activation and connectivity with prefrontal cortex during working memory updating in major depressive disorder. Neuroimage 14 43–53. 10.1016/j.nicl.2017.01.004 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Liu Z.-X., Glizer D., Tannock R., Woltering S. (2016). EEG alpha power during maintenance of information in working memory in adults with ADHD and its plasticity due to working memory training: a randomized controlled trial. Clin. Neurophysiol. 127 1307–1320. 10.1016/j.clinph.2015.10.032 [ DOI ] [ PubMed ] [ Google Scholar ]
• Ma L., Steinberg J. L., Hasan K. M., Narayana P. A., Kramer L. A., Moeller F. G. (2012). Working memory load modulation of parieto-frontal connections: evidence from dynamic causal modeling. Hum. Brain Mapp. 33 1850–1867. 10.1002/hbm.21329 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Maehler C., Schuchardt K. (2016). Working memory in children with specific learning disorders and/or attention deficits. Learn. Individ. Differ. 49 341–347. 10.1016/j.lindif.2016.05.007 [ DOI ] [ Google Scholar ]
• Mandalis A., Kinsella G., Ong B., Anderson V. (2007). Working memory and new learning following pediatric traumatic brain injury. Dev. Neuropsychol. 32 683–701. 10.1080/87565640701376045 [ DOI ] [ PubMed ] [ Google Scholar ]
• Manktelow A. E., Menon D. K., Sahakian B. J., Stamatakis E. A. (2017). Working memory after traumatic brain injury: the neural basis of improved performance with methylphenidate. Front. Behav. Neurosci. 11:58. 10.3389/fnbeh.2017.00058 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Miller G. A., Galanter E., Pribram K. H. (1960). Plans and the Structure of Behavior. New York, NY: Henry Holt and Company; 10.1037/10039-000 [ DOI ] [ Google Scholar ]
• Miyake A., Shah P. (eds) (1999). Models of Working Memory: Mechanisms of Active Maintenance and Executive Control. New York, NY: Cambridge University Press; 10.1017/CBO9781139174909 [ DOI ] [ Google Scholar ]
• Mongillo G., Barak O., Tsodyks M. (2008). Synaptic theory of working memory. Science 319 1543–1546. 10.1126/science.1150769 [ DOI ] [ PubMed ] [ Google Scholar ]
• Moore A. B., Li Z., Tyner C. E., Hu X., Crosson B. (2013). Bilateral basal ganglia activity in verbal working memory. Brain Lang. 125 316–323. 10.1016/j.bandl.2012.05.003 [ DOI ] [ PubMed ] [ Google Scholar ]
• Murty V. P., Sambataro F., Radulescu E., Altamura M., Iudicello J., Zoltick B., et al. (2011). Selective updating of working memory content modulates meso-cortico-striatal activity. Neuroimage 57 1264–1272. 10.1016/j.neuroimage.2011.05.006 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Nadebaum C., Anderson V., Catroppa C. (2007). Executive function outcomes following traumatic brain injury in young children: a five year follow-up. Dev. Neuropsychol. 32 703–728. 10.1080/87565640701376086 [ DOI ] [ PubMed ] [ Google Scholar ]
• Nadler R. T., Archibald L. M. D. (2014). The assessment of verbal and visuospatial working memory with school age canadian children. Can. J. Speech Lang. Pathol. Audiol. 38 262–279. [ Google Scholar ]
• Nakagawa S., Takeuchi H., Taki Y., Nouchi R., Sekiguchi A., Kotozaki Y., et al. (2016). Sex-related differences in the effects of sleep habits on verbal and visuospatial working memory. Front. Psychol. 7:1128. 10.3389/fpsyg.2016.01128 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Nissim N. R., O’Shea A. M., Bryant V., Porges E. C., Cohen R., Woods A. J. (2017). Frontal structural neural correlates of working memory performance in older adults. Front. Aging Neurosci. 8:328 10.3389/fnagi.2016.00328 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Oren N., Ash E. L., Tarrasch R., Hendler T., Giladi N., Shapira-Lichter I. (2017). Neural patterns underlying the effect of negative distractors on working memory in older adults. Neurobiol. Aging 53 93–102. 10.1016/j.neurobiolaging.2017.01.020 [ DOI ] [ PubMed ] [ Google Scholar ]
• Osaka M., Osaka N., Kondo H., Morishita M., Fukuyama H., Aso T., et al. (2003). The neural basis of individual differences in working memory capacity: an fMRI study. Neuroimage 18 789–797. 10.1016/S1053-8119(02)00032-0 [ DOI ] [ PubMed ] [ Google Scholar ]
• Owen A. M., McMillan K. M., Laird A. R., Bullmore E. (2005). N-back working memory paradigm: a meta-analysis of normative functional neuroimaging studies. Hum. Brain Mapp. 25 46–59. 10.1002/hbm.20131 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Owens J. A., Spitz G., Ponsford J. L., Dymowski A. R., Ferris N., Willmott C. (2017). White matter integrity of the medial forebrain bundle and attention and working memory deficits following traumatic brain injury. Brain Behav. 7:e00608. 10.1002/brb3.608 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Perbal S., Couillet J., Azouvi P., Pouthas V. (2003). Relationships between time estimation, memory, attention, and processing speed in patients with severe traumatic brain injury. Neuropsychologia 41 1599–1610. 10.1016/S0028-3932(03)00110-6 [ DOI ] [ PubMed ] [ Google Scholar ]
• Perlstein W. M., Cole M. A., Demery J. A., Seignourel P. J., Dixit N. K., Larson M. J., et al. (2004). Parametric manipulation of working memory load in traumatic brain injury: behavioral and neural correlates. J. Int. Neuropsychol. Soc. 10 724–741. 10.1017/S1355617704105110 [ DOI ] [ PubMed ] [ Google Scholar ]
• Pham A. V., Hasson R. M. (2014). Verbal and visuospatial working memory as predictors of children’s reading ability. Arch. Clin. Neuropsychol. 29 467–477. 10.1093/arclin/acu024 [ DOI ] [ PubMed ] [ Google Scholar ]
• Phillips N. L., Parry L., Mandalis A., Lah S. (2017). Working memory outcomes following traumatic brain injury in children: a systematic review with meta-analysis. Child Neuropsychol. 23 26–66. 10.1080/09297049.2015.1085500 [ DOI ] [ PubMed ] [ Google Scholar ]
• Rees K., Allen D., Lader M. (1999). The influences of age and caffeine on psychomotor and cognitive function. Psychopharmacology 145 181–188. 10.1007/s002130051047 [ DOI ] [ PubMed ] [ Google Scholar ]
• Reuter-Lorenz P. A., Cappell K. A. (2008). Neurocognitive ageing and the compensation hypothesis. Curr. Dir. Psychol. Sci. 17 177–182. 10.1111/j.1467-8721.2008.00570.x [ DOI ] [ Google Scholar ]
• Reuter-Lorenz P. A., Park D. C. (2010). Human neuroscience and the aging mind : a new look at old problems. J. Gerontol. Psychol. Sci. 65 405–415. 10.1093/geronb/gbq035 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Rieck J. R., Rodrigue K. M., Boylan M. A., Kennedy K. M. (2017). Age-related reduction of BOLD modulation to cognitive difficulty predicts poorer task accuracy and poorer fluid reasoning ability. Neuroimage 147 262–271. 10.1016/j.neuroimage.2016.12.022 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Rodriguez Merzagora A. C., Izzetoglu M., Onaral B., Schultheis M. T. (2014). Verbal working memory impairments following traumatic brain injury: an fNIRS investigation. Brain Imaging Behav. 8 446–459. 10.1007/s11682-013-9258-8 [ DOI ] [ PubMed ] [ Google Scholar ]
• Rose N. S., LaRocque J. J., Riggall A. C., Gosseries O., Starrett M. J., Meyering E. E., et al. (2016). Reactivation of latent working memories with transcranial magnetic stimulation. Science 354 1136–1139. 10.1126/science.aah7011 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Rotzer S., Loenneker T., Kucian K., Martin E., Klaver P., von Aster M. (2009). Dysfunctional neural network of spatial working memory contributes to developmental dyscalculia. Neuropsychologia 47 2859–2865. 10.1016/j.neuropsychologia.2009.06.009 [ DOI ] [ PubMed ] [ Google Scholar ]
• Schneider-Garces N. J., Gordon B. A., Brumback-Peltz C. R., Shin E., Lee Y., Sutton B. P., et al. (2010). Span, CRUNCH, and beyond: working memory capacity and the aging brain. J. Cogn. Neurosci. 22 655–669. 10.1162/jocn.2009.21230 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Schöning S., Engelien A., Kugel H., Schäfer S., Schiffbauer H., Zwitserlood P., et al. (2007). Functional anatomy of visuo-spatial working memory during mental rotation is influenced by sex, menstrual cycle, and sex steroid hormones. Neuropsychologia 45 3203–3214. 10.1016/j.neuropsychologia.2007.06.011 [ DOI ] [ PubMed ] [ Google Scholar ]
• Silvanto J. (2017). Working memory maintenance: sustained firing or synaptic mechanisms? Trends Cogn. Sci. 21 152–154. 10.1016/j.tics.2017.01.009 [ DOI ] [ PubMed ] [ Google Scholar ]
• Stegmayer K., Usher J., Trost S., Henseler I., Tost H., Rietschel M., et al. (2015). Disturbed cortico–amygdalar functional connectivity as pathophysiological correlate of working memory deficits in bipolar affective disorder. Eur. Arch. Psychiatry Clin. Neurosci. 265 303–311. 10.1007/s00406-014-0517-5 [ DOI ] [ PubMed ] [ Google Scholar ]
• Treble A., Hasan K. M., Iftikhar A., Stuebing K. K., Kramer L. A., Cox C. S., et al. (2013). Working memory and corpus callosum microstructural integrity after pediatric traumatic brain injury: a diffusion tensor tractography study. J. Neurotrauma 30 1609–1619. 10.1089/neu.2013.2934 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Vallat-Azouvi C., Weber T., Legrand L., Azouvi P. (2007). Working memory after severe traumatic brain injury. J. Int. Neuropsychol. Soc. 13 770–780. 10.1017/S1355617707070993 [ DOI ] [ PubMed ] [ Google Scholar ]
• Vartanian O., Jobidon M.-E., Bouak F., Nakashima A., Smith I., Lam Q., et al. (2013). Working memory training is associated with lower prefrontal cortex activation in a divergent thinking task. Neuroscience 236 186–194. 10.1016/j.neuroscience.2012.12.060 [ DOI ] [ PubMed ] [ Google Scholar ]
• Wang S., Gathercole S. E. (2013). Working memory deficits in children with reading difficulties: memory span and dual task coordination. J. Exp. Child Psychol. 115 188–197. 10.1016/j.jecp.2012.11.015 [ DOI ] [ PubMed ] [ Google Scholar ]
• Wylie G. R., Freeman K., Thomas A., Shpaner M., OKeefe M., Watts R., et al. (2015). Cognitive improvement after mild traumatic brain injury measured with functional neuroimaging during the acute period. PLoS One 10:e0126110. 10.1371/journal.pone.0126110 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Ziaei M., Salami A., Persson J. (2017). Age-related alterations in functional connectivity patterns during working memory encoding of emotional items. Neuropsychologia 94 1–12. 10.1016/j.neuropsychologia.2016.11.012 [ DOI ] [ PubMed ] [ Google Scholar ]
• Ziemus B., Baumann O., Luerding R., Schlosser R., Schuierer G., Bogdahn U., et al. (2007). Impaired working-memory after cerebellar infarcts paralleled by changes in bold signal of a cortico-cerebellar circuit. Neuropsychologia 45 2016–2024. 10.1016/j.neuropsychologia.2007.02.012 [ DOI ] [ PubMed ] [ Google Scholar ]
• Zylberberg J., Strowbridge B. W. (2017). Mechanisms of persistent activity in cortical circuits: possible neural substrates for working memory. Annu. Rev. Neurosci. 40 603–627. 10.1146/annurev-neuro-070815-014006 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
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PERSPECTIVE article

Memory: an extended definition.

• 1 Clinique de la Migraine de Montreal, Montreal, QC, Canada
• 2 Department of Film, Media and Cultural Studies, Birkbeck, University of London, London, United Kingdom

Recent developments in science and technology point to the need to unify, and extend, the definition of memory. On the one hand, molecular neurobiology has shown that memory is largely a neuro-chemical process, which includes conditioning and any form of stored experience. On the other hand, information technology has led many to claim that cognition is also extended, that is, memory may be stored outside of the brain. In this paper, we review these advances and describe an extended definition of memory. This definition is largely accepted in neuroscience but not explicitly stated. In the extended definition, memory is the capacity to store and retrieve information. Does this new definition of memory mean that everything is now a form of memory? We stress that memory still requires incorporation, that is, in corpore . It is a relationship – where one biological or chemical process is incorporated into another, and changes both in a permanent way. Looking at natural and biological processes of incorporation can help us think of how incorporation of internal and external memory occurs in cognition. We further argue that, if we accept that there is such a thing as the storage of information outside the brain – and that this organic, dynamic process can also be called “memory” – then we open the door to a very different world. The mind is not static. The brain, and the memory it uses, is a work in progress; we are not now who we were then.

Introduction

In the short story “Funes, the memorious,” Jorge Luis Borges invites us to imagine a man, Funes, who cannot forget anything. The narrator is ashamed in the inexactness of his retelling: his own memory is “remote and weak,” in comparison to that of his subject, which resembles “a stammering greatness.” Unlike Funes, he says, “we all live by leaving behind” – life is impossible without forgetting. He goes on to note that, even though Funes could remember every split second, he couldn’t classify or abstract from his memories. “To think is to forget a difference, to generalize, to abstract.” The reader may be led to wonder how Funes’ brain has the capacity to store all of that memory. doesn’t it reach its limits at some point? Borges leaves that question to our imagination.

In popular culture, memory is often thought of as some kind of physical thing that is stored in the brain; a subjective, personal experience that we can recall at will. This way of thinking about memory has led many to wonder if there is a maximum amount of memories we can have. But, this idea of memory is at odds with advances in the science of memory over the last century: memory isn’t really a fixed thing stored in the brain, but is more of a chemical process between neurons, which is not static. What’s more, advances in information technology are pushing our understanding of memory into new directions. We now talk about memory on a hard drive, or as a chemical change between neurons. Yet, these different definitions of memory continue to co-exist. A more narrow definition of memory, as the storage of experiences in the brain, is increasingly at odds with an extended definition, which acknowledges these advances. However, while this expanded definition is often implicitly used, it is rarely explicitly acknowledged or stated. Today, the question is no longer, how many memories can we possibly have, but, how is the vast amount of memory we process on a daily basis integrated into cognition?

In this paper, we outline these advances and the currently accepted definitions of memory, arguing that these necessarily imply that we should today adopt an extended definition. In the following, we first describe some key advances in the science of memory, cognitive theory, and information technology. These suggest to us that we are already using a unified, and extended, definition of memory, but rarely made explicit. Does this new definition of memory mean that everything is now a form of memory? We argue that looking at natural and biological processes of incorporation can help us think of how incorporation of internal and external memory occurs in cognition. Finally, we note some of the implications of this extended definition of memory.

Background: Advances in the Science of Memory

Already in the 19th century, the recognition that the number of neurons in the brain doesn’t increase significantly after reaching adulthood suggested to early neuroanatomists that memories aren’t primarily stored through the creation of neurons, but rather through the strengthening of connections between neurons ( Ramón y Cajal, 1894 ). In 1966, the breakthrough discovery of long-term potentiation (LTP) suggested that memories may be encoded in the strength of synaptic signals between neurons ( Bliss and Lømo, 1973 ). And so we started understanding memory as a neuro-chemical process. The studies by Eric Kandel of the Aplysia californica , for which he won the Nobel prize, for example, show that classical conditioning is a basic form of memory storage and is observable on a molecular level within simple organisms ( Kandel et al., 2012 ). This in effect expanded the definition of memory to include storage of information in the neural networks of simple lifeforms. Increasingly, researchers are exploring the chemistry behind memory development and recall, suggesting these molecular processes can lead to psychological adaptations (e.g., Coderre et al., 2003 ; Laferrière et al., 2011 ).

Memory is today defined in psychology as the faculty of encoding, storing, and retrieving information ( Squire, 2009 ). Psychologists have found that memory includes three important categories: sensory, short-term, and long-term. Each of these kinds of memory have different attributes, for example, sensory memory is not consciously controlled, short-term memory can only hold limited information, and long-term memory can store an indefinite amount of information.

Key to the emerging science of memory is the question of how memory is consolidated and processed. Long-term storage of memories happens on a synaptic level in most organisms ( Bramham and Messaoudi, 2005 ), but, in complex organisms like ourselves, there is also a second form of memory consolidation: systems consolidation moves, processes, and more permanently stores memories ( Frankland and Bontempi, 2005 ). Today, there are many models of how memory is consolidated in cognition. Single-system models posit that the hippocampus supports the neocortex in encoding and storing long-term memories through strengthening connections, finally leading the memory to become independent from the hippocampus (Ibid.). Multiple-trace theory instead proposes that each memory has a unique code or memory trace, which continues to involve the hippocampus to an extent ( Hintzman and Block, 1971 ; Hintzman, 1986 , 1990 ; Whittlesea, 1987 ; Versace et al., 2014 ; Briglia et al., 2018 ). In another theory, memory is understood as a form of negative entropy or rich energy ( Wiener, 1961 , 1988 ), which is then processed in a way that minimizes the expenditure of energy by the brain ( Friston, 2010 ; Van der Helm, 2016 ). Our heightened capacity to store information may be due to our ability to reduce disorder and process large amounts of information rapidly, a necessarily non-linear process ( Wiener, 1961 , 1988 ). The forgetting and fading of memories is also understood as being an important aspect of the functioning and utility of these memories ( Staniloiu and Markowitsch, 2012 ). As with a computer hard drive, memories can also be “corrupted” – false memories are commonly studied within forensic psychology ( Loftus, 2005 ). Together, these advances highlight how different kinds of memory storage are non-linear – that is, subject to complex systems interactions – contextual, and plastic. They also shed light on why, and how, we are able to live with such large quantities of information. It may not be that Funes has the special ability to remember everything, but that he lacks our ability to incorporate, and sort through, a potentially infinite amount of information.

The advance of the fields of genetics and epigenetics has also given us new metaphors to describe memory. We understand DNA as a structure that carries information that we call “genetic code” – kind of like a computer chip for biological processes. Today, the metaphor has come full circle and we can now use DNA to store and extract digital data ( Church et al., 2012 ). The study of epigenetics suggests that simple lifeforms pass on memories across generations through genetic code ( Klosin et al., 2017 ; Posner et al., 2019 ), suggesting a need to study whether humans and other complex life forms may do so as well. With these advances, our understanding of how memory is stored has expanded once again.

Further, we can now store memory in places that we haven’t been able to before. Smartphones, mind-controlled prosthetic limbs, and Google Glasses all offer new ways to store information and thereby interact with our surroundings. Our ability to produce information alters how we perceive the world, with far-reaching implications. As Stephen Hawking, the Nobel prize-winning physicist explained in his 1996 lecture, “Life in the universe,”

What distinguishes us from [our ancestors], is the knowledge that we have accumulated over the last 10000 years, and particularly, over the last three hundred. I think it is legitimate to take a broader view, and include externally transmitted information, as well as DNA, in the evolution of the human race ( Hawking, 1996 ).

The sheer quantity of available information today, as well as developments in an understanding of memory – from fixed and physical to dynamic, chemical, and a process of rich energy transfer – lead to a very different picture of memory than the one we had 100 years ago. Memory seems to exist everywhere, from an Aplysia ’s ganglion to DNA to a hard drive.

To account for these developments, cognitive scientists now propose that human cognition is actually extended beyond the brain in ways that theories of the mind did not previously recognize ( Clark and Chalmers, 1998 ; Clark, 2008 ). This approach is being called 4E cognition (Embodied, Embedded, Extended, and Enactive). For example, enactivism posits that cognition is a dynamic interaction between an organism and its environment ( Varela et al., 1991 ; Chemero, 2009 ; Menary, 2010 ; Rowlands, 2010 ; Favela and Chemero, 2016 ; Briglia et al., 2018 ). According to this framework, cognition is a process of incorporation between the environment and the body/brain/mind. To be clear, cognition is not incorporated in the surroundings, only the corpus can incorporate, and thus cognition (or what we call “mind”) is a product of the interaction between the brain, the body, and the environment.

Extending Memory

These developments indicate that we need to reconceptualize our definition of memory. What is the difference between trying to recall a childhood experience, and searching for an important email archived years ago? This distinction is best represented through the difference in how we use the words “memory” and “memories.” Usually, “memories” tends to refer to events recalled from the past, which are seen as more representational and subjective. In contrast, “memory” now is used to refer to storage of information in general , including in DNA, digital information storage, and neuro-chemical processes. Today, science has moved far beyond a popular understanding of memory as fixed, subjective, and personal. In the extended definition, it is simply the capacity to store and retrieve information . To illustrate why memory has extended beyond this original use, we want to ask the reader: what do a stressed-out driver and a snail have in common?

(1). A homeowner has been trying to sell her house for a year, and worrying about it. One day, she’s driving to work and becomes extremely anxious, for no apparent reason. She wasn’t thinking of anything in particular at the time. Confused, she looks around, and notices a billboard advertising a real estate agency. She realizes that she had seen it out of the corner of her eye, and her brain had then processed the information while she was thinking of something else, which then triggered the anxiety attack.

(2). Consider a nerve cell of an A. californica , a kind of sea snail, which is prodded vigorously for a short time period, provoking an immediate withdrawal response. Shortly afterward, it is prodded less intensely, but, it elicits the same withdrawal response. It is found that the slugs’ nerve cell is sensitive for up to 24 h – the nerve cells “remember” past pain.

Each example illustrates a different kind of chemical, biological process. In the first example, an outside stimulus triggers a stress response for the homeowner. We can surmise that though she didn’t “remember” anything, non-consciously, she did. In the second example, the snail certainly “remembers” the provocation, even though this memory is only stored in a few cells. But can we really call this memory?

However, on closer examination, we are forced to concede that each of them should be called a form of memory. First, consider the homeowner: her brain “remembers” something that does not occur to her as a conscious thought. It is clearly a chemical process occurring in the background. Most would grant that this would nevertheless be a form of memory, as it involves recalling information stored in her brain. Already, a broader definition of memory is used that does not imply conscious attention. Now, consider the snail: it is also storing information chemically. Once again, this does not involve a conscious, subjective process of storing and remembering – it is purely reactive, but information is being stored and recalled nonetheless. We would need to concede that if the homeowner’s experience counts as memory, then the slug’s automatic response does as well. There is in fact little difference between the first two examples: there is a transfer of information that causes a reaction. Both should be considered forms of memory.

A Slippery Slope?

If we agree with this expanded definition of memory, then it follows that experience is also a form of stored information, kinds of memory . We are not saying that a particular experience, as an event , is a memory. Rather, we here use the word “experience” as connoted by the phrase “an experienced driver,” an “experienced writer.” They have a set of experiences, remembered through practice, and retrieved when they drive, or write. When we accumulate knowledge, information, and techniques, then the accumulation of those separate processes constitute experience . This experience involves retrieval of information, conversely, being experienced is the process of retrieving memory.

Under this definition, even immunological and allergy processes may be considered memory. There is a storage of information of the allergen or the viral/bacterial aggressor and when the aggressor or allergen re-appears there is a cascade of inflammatory processes. This can be considered the storage and retrieval of information, and thus a form of memory. This does not contradict the accepted definition of memory within psychology, as it is still seen as the ability to encode, store, and recall information. Rather, it extends it to processes not just bound by the brain.

If memory is indeed defined as “the capacity to store and/or retrieve information,” then this may lead anyone to ask – what isn’t memory? Wouldn’t this definition of memory be far too broad, and include a vast range of phenomena? Is the extended definition of memory, as is being proposed by neurobiologists and cognitive theorists, a slippery slope?

As we suggested above, however, memory still involves a process of incorporation, that is, requiring a corpus . While memory may be stored on the cloud, it requires a system of incorporation with the body and therefore the mind. In other words, the “cloud” by itself is not memory, but operates through an infrastructure (laptops, smart phones, Google Glasses) that are integrated with the brain-mind through learned processes of storage and recall. The conditioning of an Aplysia ’s ganglion is incorporated into an organism. Memory, it seems, is not just mechanistic, but a dynamic process. It is a relationship – where one biological or chemical process is incorporated into another, and changes both in a permanent way. A broadened definition must account for this dynamic relationship between organisms and their environment.

How can we understand this process of incorporation? It appears that symbiotic incorporation of biological processes is quite common in nature. Recent studies offer more evidence that early cells acquired mitochondria by, at some point, incorporating external organisms into their own cell structure ( Thrash et al., 2011 ; Ferla et al., 2013 ). Mitochondria have their own genome, which is similar to that of bacteria. What was once a competitor and possibly a parasite became absorbed into the organism – and yet, the mitochondrion was not fully incorporated and retains many of its own processes of self-organization and memory storage, separate from the cell it resides in. This evolutionary process highlights the way by which external properties may become incorporated into the internal, changing both. Looking at natural and biological processes of incorporation can help us think of how incorporation of internal and external memory occurs in cognition.

Implications

This extended definition of memory may seem ludicrous and hard to accept. You may be tempted to throw up your hands and go back to the old, restricted, definition of memory – one that requires the transmission of subjective memories.

We beg you not to. There are several benefits of this approach to memory. First, in biology, expanding the definition of memory helps us shift from a focus on “experience” (which suggests an immaterial event) to a more material phenomenon: a deposit of events that may be stored and used afterward. By expanding the concept of memory, the study of memory within molecular neurobiology becomes more relevant and important. This expanded definition is in large part already widely accepted, for example, in Kandel’s Aplysia , conditioning is acknowledged to be a part of memory, and memory is not a part of conditioning. Memory would become the umbrella for learning, conditioning, and other processes of the mind/brain. Doing so changes the frame of observation from one which understands memory as a narrow, particular process, to one which understands it as a dynamic, fluid, and interactive phenomenon, neither just chemical or digital but integrated into our experience through multiple media. Second, it helps to conceptualize the relationship between biology, psychology, cognitive science, and computer science – as all three involve studying the transfer of information.

Third, it opens up an interesting way to imagine our own future. If we accept that there is such a thing as the storage of information outside the brain – and that this organic, dynamic process can also be called “memory” – then we open the door to a very different world. The mind is not static. Rather, like early cells acquiring mitochondria, it incorporates information from its surroundings, which in turn changes it. The brain, and the memory it uses, is a work in progress; we are not now who we were then. Many have already noted the extent to which we are cyborgs ( Harraway, 1991 ; Clark, 2003 , 2005 ); this neat line between human and technology may become more and more blurred as we develop specialized tools to store all kinds of information in our built environment. In what ways will the mind-brain function differently as it becomes increasingly more incorporated in its milieu, relying on it for information storage and processing?

Now let’s talk about Funes. His inability to forget his memories may seem familiar to some, a metaphor for our current condition. We may now recognize a bit of ourselves in him: we don’t see limits in our capacity to store new information, and the sheer availability of it is sometimes overwhelming. Even without the arrival of the Information Age, we carry with us through life a heavy load of disappointments, broken dreams, little tragedies and many memories. We know that forgetting is a must and a challenge. Yet, we are learning rapidly how to incorporate and use the massive amounts of data now available to us. The main challenge for each of us is to harness and control the unleashed powers given to us by technology. The future is uncertain, but some things remain the same. As Kandel (2007 , p. 10) wrote, “We are who we are in great measure because of what we learn, and what we remember.”

Author Contributions

GZ and AV drafted and edited the manuscript. Both authors contributed to manuscript revision, read, and approved the submitted version.

Conflict of Interest

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.

Acknowledgments

The authors wish to thank Michael Lifshitz, Ph.D. for reading an early copy of this article and providing feedback. The authors also wish to thank Steven J. Lynn, Alan M. Rapoport, and Morgan Craig for the feedback and encouragement.

Bliss, T., and Lømo, T. (1973). Long-lasting potentiation of synaptic transmission in the dentate area of the anaesthetized rabbit following stimulation of the perforant path. J. Physiol. 232, 331–356. doi: 10.1113/jphysiol.1973.sp010273

CrossRef Full Text | Google Scholar

Bramham, C. R., and Messaoudi, E. (2005). BDNF function in adult synaptic plasticity: the synaptic consolidation hypothesis. Prog. Neurobiol. 76, 99–125. doi: 10.1016/j.pneurobio.2005.06.003

PubMed Abstract | CrossRef Full Text | Google Scholar

Briglia, J., Servajean, P., Michalland, A. H., Brunel, L., and Brouillet, D. (2018). Modeling an enactivist multiple-trace memory. ATHENA: a fractal model of human memory. J. Math. Psychol. 82, 97–110. doi: 10.1016/j.jmp.2017.12.002

Chemero, A. (2009). Radical Embodied Cognitive Science. Cambridge, MA: MIT Press.

Google Scholar

Church, G. M., Gao, Y., and Kosuri, S. (2012). Next-generation digital information storage in DNA. Science 337:1628. doi: 10.1126/science.1226355

Clark, A. (2003). Natural-Born Cyborgs: Minds, Technologies, and the Future of Human Intelligence. New York, NY: Oxford University Press.

Clark, A. (2005). “Intrinsic content, active memory and the extended mind”. Analysis 65, 1–11. doi: 10.1111/j.1467-8284.2005.00514.x

Clark, A. (2008). Supersizing the Mind: Embodiment, Action, and Cognitive Extension OUP. Oxford: Oxford University Press.

Clark, A., and Chalmers, D. (1998). The extended mind. Analysis 58, 7–19.

Coderre, T. J., Mogil, J. S., and Bushnell, M. C. (2003). “The biological psychology of pain,” in Handbook of Psychology , eds M. Gallagher, and R. J. Nelson, (New York, NY: Wiley), 237–268.

Favela, L. H., and Chemero, A. (2016). “The animal-environment system,” in Foundations of Embodied Cognition: Perceptual and Emotional Embodiment , eds Y. Coello, and M. H. Fischer, (New York, NY: Routledge/Taylor & Francis Group), 59–74.

Ferla, M. P., Thrash, J. C., Giovannoni, S. J., and Patrick, W. M. (2013). New rRNA gene-based phylogenies of the Alphaproteobacteria provide perspective on major groups, mitochondrial ancestry and phylogenetic instability. PLoS One 8:e83383. doi: 10.1371/journal.pone.0083383

Frankland, P. W., and Bontempi, B. (2005). The organization of recent and remote memories. Nat. Rev. Neurosci. 6, 119–130. doi: 10.1038/nrn1607

Friston, K. (2010). The free-energy principle: a unified brain theory? Nat. Rev. Neurosci. 11, 127–138. doi: 10.1038/nrn2787

Harraway, D. J. (1991). Simians, Cyborgs, and Women: The Reinvention of Nature . New York: Routledge.

Hawking, S. (1996). Life in the Universe. Available at: http://www.hawking.org.uk/life-in-the-universe.html (accessed February 2, 2019).

Hintzman, D. L. (1986). “Schema abstraction” in a multiple-trace memory model. Psychol. Rev. 93, 411–428. doi: 10.1037//0033-295x.93.4.411

Hintzman, D. L. (1990). Human learning and memory: connections and dissociations. Annu. Rev. Psychol. 41, 109–319.

Hintzman, D. L., and Block, R. A. (1971). Repetition and memory: evidence for a multiple trace hypothesis. J. Exp. Psychol. 88, 297–306. doi: 10.1037/h0030907

Kandel, E. R. (2007). In Search of Memory: The Emergence of a New Science of Mind. New York, NY: WW Norton & Company.

Kandel, E. R., Schwartz, J. H., Jessell, T. M., Siegelbaum, S. A., and Hudspeth, A. J. (2012). Principles of Neural Science , 5th Edn. New York, NY: McGraw-Hill.

Klosin, A., Casas, E., Hidalgo-Carcedo, C., Vavouri, T., and Lehner, B. (2017). Transgenerational transmission of environmental information in C. elegans. Science 356, 320–323. doi: 10.1126/science.aah6412

Laferrière, A., Pitcher, M. H., Haldane, A., Huang, Y., Cornea, V., Kumar, N., et al. (2011). PKMζ is essential for spinal plasticity underlying the maintenance of persistent pain. Mol. Pain 7:99. doi: 10.1186/1744-8069-7-99

Loftus, E. F. (2005). Planting misinformation in the human mind: a 30-year investigation of the malleability of memory. Learn. Mem. 12, 361–366. doi: 10.1101/lm.94705

Menary, R. (2010). Introduction to the special issue on 4E cognition. Phenomenol. Cogn. Sci. 9, 459–463. doi: 10.1007/s11097-010-9187-6

Posner, R., Toker, I. A., Antonova, O., Star, E., Anava, S., Azmon, E., et al. (2019). Neuronal small RNAs control behavior transgenerationally. Cell 177:1814-1826.e15. doi: 10.1016/j.cell.2019.04.029

Ramón y Cajal, S. (1894). The croonian lecture: la fine structure des centres nerveux. Proc. R. Soc. Lond. 55, 444–468. doi: 10.1098/rspl.1894.0063

Rowlands, M. (2010). The New Science of the Mind: From Extended Mind to Embodied Phenomenology. Cambridge, MA: MIT Press.

Squire, L. R. (2009). Memory and brain systems: 1969–2009. J. Neurosci. 29, 12711–12716. doi: 10.1523/jneurosci.3575-09.2009

Staniloiu, A., and Markowitsch, H. J. (2012). Towards solving the riddle of forgetting in functional amnesia: recent advances and current opinions. Front. Psychol. 3:403. doi: 10.3389/fpsyg.2012.00403

Thrash, J. C., Boyd, A., Huggett, M. J., Grote, J., Carini, P., Yoder, R. J., et al. (2011). Phylogenomic evidence for a common ancestor of mitochondria and the SAR11 clade. Sci. Rep. 1:13. doi: 10.1038/srep00013

Van der Helm, P. A. (2016). Structural coding versus free-energy predictive coding. Psychonom. Bull. Rev. 23, 663–677. doi: 10.3758/s13423-015-0938-9

Varela, F. J., Thompson, E., and Rosch, E. (1991). The Embodied Mind: Cognitive Science and Human Experience. Boston, MA: MIT Press.

Versace, R., Vallet, G. T., Brunel, L., Riou, B., Lesourd, M., and Labeye, E. (2014). ACT-IN: an integrated view of memory mechanisms. J. Cogn. Psychol. 26, 280–306. doi: 10.1080/02699931.2017.1387101

Whittlesea, B. W. A. (1987). Preservation of specific experiences in the representation of general knowledge. Cognition 13, 3–17. doi: 10.1037//0278-7393.13.1.3

Wiener, N. (1961). Cybernetics: or Control and Communication in the Animal and the Machine , 2nd Edn. Cambridge, MA: MIT Press.

Wiener, N. (1988). The Human Use of Human Beings: Cybernetics and Society (No. 320). Cambridge, MA: Da Capo Press.

Keywords : memory, extended cognition, incorporation, storage of information, information technology, experience

Citation: Zlotnik G and Vansintjan A (2019) Memory: An Extended Definition. Front. Psychol. 10:2523. doi: 10.3389/fpsyg.2019.02523

Received: 26 July 2019; Accepted: 24 October 2019; Published: 07 November 2019.

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Copyright © 2019 Zlotnik and Vansintjan. 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(s) 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: Gregorio Zlotnik, enZpZ3JlZ29AZ21haWwuY29t ; Aaron Vansintjan, YXZhbnNpbnRqYW5AZ21haWwuY29t

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