Working Memory From the Psychological and Neurosciences Perspectives: A Review

Abstract

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.

Full text

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REVIEW
published: 27 March 2018
doi: 10.3389/fpsyg.2018.00401
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
aini_ismafairus@usm.my
Specialty section:
This article was submitted to
Cognitive Science,
a section of the journal
Frontiers in Psychology
Received: 24 November 2017
Accepted: 09 March 2018
Published: 27 March 2018
Citation:
Chai WJ, Abd Hamid AI and
Abdullah JM (2018) Working Memory
From the Psychological
and Neurosciences Perspectives:
A Review. Front. Psychol. 9:401.
doi: 10.3389/fpsyg.2018.00401
Working Memory From the
Psychological and Neurosciences
Perspectives: A Review
Wen Jia Chai1, Aini Ismafairus Abd Hamid1,2*and Jafri Malin Abdullah1,2
1Department of Neurosciences, School of Medical Sciences, Universiti Sains Malaysia, Kubang Kerian, Malaysia,
2Center for Neuroscience Services and Research, Universiti Sains Malaysia, Kubang Kerian, Malaysia
Since the concept of working memory was introduced over 50 years ago, different
schools of thought have offered different definitions for working memory based on
the various cognitive domains that it encompasses. The general consensus regarding
working memory supports the idea that working memory is extensively involved in
goal-directed behaviors in which information must be retained and manipulated to
ensure successful task execution. Before the emergence of other competing models,
the concept of working memory was described by the multicomponent working memory
model proposed by Baddeley and Hitch. In the present article, the authors provide an
overview of several working memory-relevant studies in order to harmonize the findings
of working memory from the neurosciences and psychological standpoints, especially
after citing evidence from past studies of healthy, aging, diseased, and/or lesioned
brains. In particular, the theoretical framework behind working memory, in which the
related domains that are considered to play a part in different frameworks (such as
memory’s capacity limit and temporary storage) are presented and discussed. From the
neuroscience perspective, it has been established that working memory activates the
fronto-parietal brain regions, including the prefrontal, cingulate, and parietal cortices.
Recent studies have subsequently implicated the roles of subcortical regions (such
as the midbrain and cerebellum) in working memory. Aging also appears to have
modulatory effects on working memory; age interactions with emotion, caffeine and
hormones appear to affect working memory performances at the neurobiological level.
Moreover, working memory deficits are apparent in older individuals, who are susceptible
to cognitive deterioration. Another younger population with working memory impairment
consists of those with mental, developmental, and/or neurological disorders such as
major depressive disorder and others. A less coherent and organized neural pattern
has been consistently reported in these disadvantaged groups. Working memory
of patients with traumatic brain injury was similarly affected and shown to have
unusual neural activity (hyper- or hypoactivation) as a general observation. Decoding
the underlying neural mechanisms of working memory helps support the current
theoretical understandings concerning working memory, and at the same time provides
insights into rehabilitation programs that target working memory impairments from
neurophysiological or psychological aspects.
Keywords: working memory, neuroscience, psychology, cognition, brain, central executive, prefrontal cortex,
review
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Chai et al. A Review of Working Memory
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).
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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
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TABLE 1 | Working memory (WM) studies in the healthy brain.
Authors WM Components WM Task Neuroimaging Modality Brain Regions Involved
Bolkan et al., 2017
(animal study: mice)
WM maintenance Spatial WM task (T-maze) – MD, medial PFC
Chein et al., 2011 WM storage and
processing/recall
Complex WM span task fMRI PFC, ACC, PPC, MTL
Jimura et al., 2017 Cognitive control; WM load Intertemporal decision-making
task; Sternberg WM task
fMRI anterior PFC, DLPFC, IFJ, pre-SMA, AI,
PPC, tempo-parietal junction
Kim et al., 2015 Information integration Arithmetic task fMRI IFG, MFG, FPC, DLPFC
Moore et al., 2013 WM encoding, maintenance,
and retrieval
Verbal WM task fMRI MFG, IFS, DLPFC, caudate, thalamus,
parietal and cingulate regions
Murty et al., 2011 Selective updating Digital-updating WM task fMRI DLPFC, caudate, SN/VTA, parietal,
cerebellar, and cingulate regions
Osaka et al., 2003 WM capacity Verbal WM task fMRI PFC, ACC, STG
Vartanian et al., 2013 WM capacity N-back task; AUT fMRI DLPFC, VLPFC, anterior PFC, OFC,
SMA
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.
FIGURE 1 | A simplified depiction (adapted from the multicomponent working memory model by Baddeley, 2010) as implicated in the brain, in which the central
executive assumes the role to exert control and oversee the manipulation of incoming information for intended execution. ACC, Anterior cingulate cortex.
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.
Further investigation has recently revealed that other than
the generally informed cortical structures involved in verbal
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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
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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 (Mage = 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
(Mage = 22.6) and (2) older (Mage = 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
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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.
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).
In addition, patients with major depressive disorder (MDD)
displayed weaker performance in the working memory updating
domain in which information manipulation was needed when
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TABLE 2 | Working memory (WM) studies in relation to age.
Authors Target groups WM task Neuroimaging modality Outcome variables
Arendash et al., 2006
(animal study: Mice)
Heterozygous male mice
carrying the mutant APPK670N,
M671L gene (APPsw)
Radial Arm Water Maze – Behavioral task performances
Borella et al., 2017 Old (Mage = 68.5) CWMS task; LST; The Jigsaw
Puzzle test – Puzzle
– Neuropsychological test
performances after training
Dahlin et al., 2008 Old (Mage = 68.3) Computation span task;
Forward and backward digit
span (WAIS); N-back task
– Neuropsychological test
performances after training
Duff and Hampson,
2000
Post-menopausal women
(Mage = 55.7)
Digit-ordering task; Spatial
working-memory task
– Neuropsychological test
performances
Guye and von Bastian,
2017
Old (Mage = 70.2) Complex span task; binding
task; memory updating task
– Neuropsychological test
performances after training
Haller et al., 2017 Subtle cognitive deterioration;
Mild cognitive impairments
N-back task fMRI Functional connectivity after
caffeine intake
Haller et al., 2013 Old (Mage = 68.8) N-back task fMRI; MRI Brain activations in ROIs;
Functional connectivity;
Baseline perfusion
Joseph et al., 2012 Pre-menopausal women N-back task fMRI Brain activations in ROIs
Klaassen et al., 2013 Middle-aged (Mage = 49.2) Letter Sternberg task fMRI Brain activations in ROIs
Nissim et al., 2017 Old (Mage = 70.3) N-back task MRI Cortical surface area; cortical
thickness
Oren et al., 2017 Old (Mage = 71.8) Emotional n-back task fMRI Functional connectivity
Rees et al., 1999 Young- (Mage = 23.5) and
middle-aged (Mage = 56.5)
Digit span task – Neuropsychological test
performances
Rieck et al., 2017 Across the lifespan
(age = 20–94)
Spatial distance judgment task fMRI Brain activations to task
difficulty
Ziaei et al., 2017 Old (Mage = 68.2) Visual WM task with emotional
distractors
fMRI Functional connectivity
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.
TABLE 3 | Working memory (WM) studies in the diseased brain.
Authors Target groups WM task Neuroimaging modality Outcome variables
Ashkenazi et al., 2013 Mathematical disabilities WMTB-C fMRI Brain activations in ROIs
Balderston et al., 2017 Generalized/Social anxiety disorder N-back WM task fMRI Brain activations in ROIs
Grot et al., 2017 Schizophrenia WM binding task fMRI Brain activations in ROIs
Le et al., 2017 MDD Delayed recognition task fMRI Functional connectivity
Maehler and Schuchardt, 2016 Dyslexia; dyscalculia; ADHD Phonological, visuospatial, and
central executive tasks
– Neuropsychological test
performances
Rotzer et al., 2009 Developmental dyscalculia Corsi Block Tapping test fMRI Brain activations in ROIs
Stegmayer et al., 2015 Bipolar affective disorder Verbal delayed matching to
sample task
fMRI Functional connectivity
Wang and Gathercole, 2013 Reading difficulties AWMA – Neuropsychological test
performances
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.
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
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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
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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
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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.
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
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TABLE 4 | Working memory (WM) studies in the TBI group.
Authors Target groups WM task Neuroimaging
modality
Outcome variables
Bailey et al., 2017 MDD; MDD-TBI; TBI Sternberg task EEG Functional gamma connectivity
Chen et al., 2012 Mild TBI N-back WM task fMRI Brain activations
Dobryakova et al., 2015 Moderate and severe TBI CapMan task fMRI Effective connectivity
Ellis et al., 2016 Pediatric moderate and severe TBI – EEG (Visual
ERP)
Interhemispheric transfer time
Gorman et al., 2012 Pediatric TBI CLS; CLS-DT; VSS; VSS-DT – Neuropsychological test
performances
Hillary et al., 2011 Moderate and severe TBI N-back WM task fMRI Effective connectivity
Hsu et al., 2015 Mild TBI N-back WM task fMRI Brain activations in ROIs
Mandalis et al., 2007 Pediatric moderate and severe TBI Forward digit span task
(WISC-III); TOL; COWAT; The
animal fluency test
– Neuropsychological test
performances
Manktelow et al., 2017 Moderate and severe TBI N-back WM task; Rapid Visual
Information Processing Task;
Stop Signals Task; TOL
DTI; fMRI Structural and functional
connectivity after
methylphenidate administration
Rodriguez Merzagora et al., 2014 Moderate and severe TBI Verbal n-back WM task fNIRS Hemodynamic responses in
ROIs
Owens et al., 2017 Moderate and severe TBI Selective attention tasks;
N-back WM task
DTI Structural connectivity in medial
forebrain bundle
Perbal et al., 2003 Severe TBI Corkin WM Test – Neuropsychological test
performances
Perlstein et al., 2004 TBI Visual n-back WM task fMRI Brain activations
Treble et al., 2013 Pediatric TBI CLS-DT; VSS-DT DTI Structural connectivity in
corpus callosum
Wylie et al., 2015 Mild TBI N-back WM task fMRI Brain activations
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.
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, gand 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
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Chai et al. A Review of Working Memory
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.
CONCLUSION
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.
FUNDING
This work was supported by the Transdisciplinary Research
Grant Scheme (TRGS) 203/CNEURO/6768003 and the USAINS
Research Grant 2016.
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Conflict of Interest Statement: The authors declare that the research was
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The reviewer EB and handling Editor declared their shared affiliation.
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Frontiers in Psychology | www.frontiersin.org 16 March 2018 | Volume 9 | Article 401