Opening Questions in Visual Working Memory
Anna C. Nobre
Abstract
■ In this reflective piece on visual working memory, I depart
from the laboriously honed skills of writing a review. Instead of
integrating approaches, synthesizing evidence, and building a
cohesive perspective, I scratch my head and share niggles and
puzzlements. I expose where my scholarship and understanding
are stumped by findings and standard views in the literature. ■
The act of self-exposure on paper is unnerving. However,
confronting ignorance is a necessary and important step
for scientific advancement. In private or, even better, in
the good company of a close colleague, surfacing and shar-
ing bafflements can become the most rewarding source of
creative energy. For me, many new experimental designs,
methods, and conceptualizations were born from open-
ended conversations with kindred science friends, com-
paring and discussing points of contention and confusion.
I have been lucky to enjoy the friendship of some brilliant,
curious, generous, and unpretentious collaborators. Mark
Stokes stands out, playing a vital role in shaping and
unshaping my ideas and methods. I offer this soliloquy
as a snapshot of what I might bring to such conversations.
I do so in the spirit of spreading the courage and fun of fac-
ing and respecting ignorance before building knowledge.
Science shows humankind at its best. By building on evi-
dence with curiosity, ingenuity, logic, and years of prepa-
ration, scientists expose the nature of matter, life, and
mind with increasing sophistication and dazzling discover-
ies. Yet, as a human endeavor, science cannot escape our
cognitive limitations or the social and political dimensions
of our behavior. Thus, experimental paradigms1 develop
not only for the quality of the methods and the robustness
of the results that ground models and theories. They also
reflect the technologies of the times, the influence of
powerful and charismatic individuals, clannish allegiances
driving competition and cooperation, the catchiness of
simplistic and categorical ideas, the lure of the novel,
and an occasional disregard for earlier foundational dis-
coveries. Clear dichotomies and intuitive models that
appeal to folk psychology often have an edge over better
nuanced and integrative views.
Science progresses nonetheless, although not without
occasionally derailing, reinventing, or getting stuck in local
minima. Promoting progress takes seeing through the
University of Oxford
standard accepted narratives and models in one’s field
and looking anew with scholarship and a beginner’s
mindset.
DEFINING VISUAL WORKING MEMORY
Working memory is a core psychological construct essen-
tial for guiding flexible and adaptive human behavior. The
term working memory was probably first introduced by
Miller, Galanter, and Pribram (1960) to refer to a quick-
access form of memory for executing plans. In their pro-
posal, uncompleted parts of plans in working memory
comprise intentions. Atkinson and Shiffrin (1968) also
used the term in their seminal theoretical treatment of
memory and its control processes. In their proposal, the
short-term memory store makes up the person’s working
memory, receiving selected inputs from a brief sensory
register and the long-term memory store. Flexible and
volitional control processes operate in the short-term
store, helping to support storage, search, and retrieval.
Both accounts echo the information-processing paradigm
inspired by advances in communication and computer
technologies during the inception of modern cognitive
psychology (Neisser, 1967). Baddeley and Hitch (1974)
later used the term to describe a specific, multicomponent
model of short-term maintenance and manipulation of
visual and verbal material. This highly popular model
and its various alterations (Baddeley, Hitch, & Allen,
2019; Baddeley, 2012) have heavily shaped research, espe-
cially in the verbal domain.
Currently, there is some confusion and disagreement
concerning how the term “working memory” should be
used. Some scholars reserve it for the multicomponent
model (Baddeley & Hitch, 1974); others use it more gen-
erally but contend it implies necessarily more than just
temporary storage, requiring the manipulation of contents
and/or conscious access. This piece adopts a more basic,
bare-bones definition: the temporary storage of visual
contents to guide behavior. The simple definition
© 2022 Massachusetts Institute of Technology. Published under a
Creative Commons Attribution 4.0 International (CC BY 4.0) license.
Journal of Cognitive Neuroscience 35:1, pp. 49–59
https://doi.org/10.1162/jocn_a_01920
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preserves the essential role in guiding future behavior
from the original definition (Miller et al., 1960) and the sep-
aration between mnemonic content and control processes
operating upon them (Atkinson & Shiffrin, 1968).
UPDATING THE STANDARD PARADIGM
Until recently, most research on visual working memory
examined its representational aspects—the nature of its
contents and the related psychological and neural mecha-
nisms supporting storage. The standard view was that
visual working memory holds contents in a robust format,
and these are accessed through an inflexible process of
exhaustive search (Sternberg, 1966; Sperling, 1960).
Capacity is severely limited, and dichotomies arose to
explain the limitations. Polar positions propose that
resources are distributed evenly across the contents to
be memorized (Ma, Husain, & Bays, 2014; Bays & Husain,
2008) versus that a fixed number of slots are available for
contents to occupy (Zhang & Luck, 2008). Another related
dichotomy concerns whether there is only one item in the
focus of visual working memory (Oberauer, 2002) or the
full set of items within the capacity limitations (Cowan,
2010). The primary mechanism proposed for maintaining
contents in visual working memory is persistent “delay”
activity in neuronal populations signaling attributes of
memorized items in prefrontal and sensory areas (Kojima
& Goldman-Rakic, 1982; Fuster & Alexander, 1971; Kubota
& Niki, 1971). The delay activity during working memory is
considered a major top–down source for selective atten-
tion, guiding sensory processing of task-relevant items
(Desimone & Duncan, 1995). The focusing of perception
by working memory thus feeds a virtuous cycle, ultimately
helping encode relevant memory content, which in turn
guides sensory processing (Nobre & Stokes, 2011).
Current research is breaking from the standard visual
working-memory paradigm and upgrading our under-
standing in various ways.
Work in my group has promoted the realization that
visual working memory is much more flexible than previ-
ously envisaged, with selective attention continuing to
operate within visual working memory to prioritize main-
tenance, selection, and access to mnemonic contents (van
Ede & Nobre, 2023; Nobre & Stokes, 2019). Initial studies
revealed that retroactive attention cues (retrocues) during
the maintenance period indicating the memory content
that is relevant for the upcoming task confer significant
performance benefits (Griffin & Nobre, 2003; Landman,
Spekreijse, & Lamme, 2003). Subsequent studies added
observations of selective attention modulating working
memory based on changing internal states in the absence
of cueing stimuli (van Ede, Niklaus, & Nobre, 2017), sen-
sory capture by external stimuli sharing features with
working-memory content (van Ede, Board, & Nobre,
2020), and intended actions prioritizing congruent items
(Heuer, Ohl, & Rolfs, 2020). Selective attention can mod-
ulate feature-based (Niklaus, Nobre, & van Ede, 2017; Ye,
Hu, Ristaniemi, Gendron, & Liu, 2016) and object-based
(Lin, Kong, & Fougnie, 2021; Peters, Kaiser, Rahm, &
Bledowski, 2015) information in visual working memory
(Hajonides, van Ede, Stokes, & Nobre, 2020), with other
possible substrates still to be tested. Interestingly, selec-
tive attention in visual working memory is also revers-
ible, such that priorities can shift flexibly among contents
without obligatory trade-offs in their quality or accessibility
(Zokaei, Board, Manohar, & Nobre, 2019; Myers,
Chekroud, Stokes, & Nobre, 2018; van Ede et al., 2017;
Rerko & Oberauer, 2013; Lewis-Peacock, Drysdale,
Oberauer, & Postle, 2012).
In addition to dispelling the notion of an inflexible store,
the growing literature on selective attention in visual work-
ing memory also challenges other standard views and
opens new questions. The ability to juggle selective atten-
tion among contents reversibly argues against strong pro-
posals of fixed resource allocation within capacity limits
(Myers, Stokes, & Nobre, 2017). The ability to modulate
features and object-level representations questions the
nature of unitary entities for slots. Reconsidering such
concepts as “resources” and “slots” also highlights their
underspecified nature, which compromises their utility
as theoretical tools. The influence of intended actions
on setting internal attention reminds us of the ecological
purpose of visual working memory. Whereas emphasis
has traditionally been placed on representational proper-
ties, the most important aspect of visual working memory
is its function of preparing for future behavior (van Ede,
2020; Myers et al., 2017; see also Fuster & Alexander,
1971). Accordingly, studies re-introducing action links to
visual stimuli in working-memory tasks have underlined
the major role of action readiness (Boettcher, Gresch,
Nobre, & van Ede, 2021; van Ede, Chekroud, Stokes, &
Nobre, 2019; Schneider, Barth, & Wascher, 2017).
The paradigm is thereby shifting from visual working
memory as an inflexible representational state that is
severely limited in capacity to a highly flexible pragmatic
functional state that adaptively prepares the individual
for potential futures and guides action (van Ede & Nobre,
2023). However, this updating of views deepens old ques-
tions and opens new ones. Much effort lies ahead before a
full understanding emerges of how signals derived from
experience persist to constitute structured content and
prepare possible actions; how control processes, includ-
ing but not limited to selective attention, interact with
such traces; and how they relate to long-term traces, both
pre-existing long-term memories that scaffold perception
and those yet to form based on the recent sensory input.
Addressing these puzzles will ultimately require looking
under the hood at the changing patterns, dynamics, and
transformations of activity in neurons and networks.
NEURAL TRACES
The dissociation between working memory and long-term
memory started as a theoretical one. James (1890), for
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example, contrasted primary memory (working memory),
as never cut off in consciousness from the present
moment, with secondary memory (long-term memory),
as the knowledge of a former state already dropped from
consciousness. The case of patient HM (Scoville & Milner,
1957) provided a striking neuropsychological manifesta-
tion of the distinction. Whereas HM had profound epi-
sodic amnesia, he was nevertheless able to maintain
information in mind over short periods if not interrupted,
using elaborate strategies if required (Milner, Corkin, &
Teuber, 1968).
Yet, the nature of the relation between working mem-
ory and long-term memory is still unresolved. Some of
the initial neuropsychological evidence that grounded the-
ories proposing a strong separation between their neural
systems may have been overinterpreted (e.g., Warrington
& Shallice, 1972). For example, these dissociations were
often based on different types of content and tasks with
varied performance demands. More recently, some
scholars emphasize the association between neural sys-
tems supporting working memory and long-term memory
for equivalent types of material (e.g., Zokaei, Nour, et al.,
2019; Pertzov et al., 2013; Graham, Barense, & Lee, 2010;
Olson, Moore, Stark, & Chatterjee, 2006). However, even if
the memory systems are co-extensive, the neural pro-
cesses supporting information maintenance over short
and long spans are likely to differ, given the time required
for cementing the structural changes involved in enduring
plasticity.
Interestingly, early theoretical models are often misin-
terpreted as proposing a strong division between the
memory systems, whereas they did “not require that the
two stores necessarily be in different parts of the brain
or involve different physiological structures” (Atkinson &
Shiffrin, 1971). My own working hypothesis is that overlap-
ping brain areas are involved in storing contents over short
and long timespans and that multiple functions operate
upon these traces to facilitate performance based on work-
ing memory or long-term memory (e.g., selecting, inte-
grating, individuating, relating, or organizing contents).
However, I have not completely ruled out the possibility
that some brain structure(s) may be specifically recruited
for an adaptive temporary transient store for task-relevant
content (see Xu, 2017; Duncan, 2001).
A clear theoretical proposal for distinct processes to
support traces over the short and long term in the brain
was introduced by Hebb (1949). He suggested that lin-
gering reverberatory activity within cell assemblies was
sufficient for short-term storage, whereas enduring
strengthening of functional connections among neurons
with correlated activity (through growth process or met-
abolic change) supported long-term storage.
Kubota & Niki, 1971) and then in many other brain regions
(see Christophel, Klink, Spitzer, Roelfsema, & Haynes,
2017) provided a simple and intuitive solution for
working-memory maintenance. The idea stuck and has
become the standard textbook explanation for working-
memory storage.
There are many appealing aspects to the explanation.
Persistent neuronal firing yields a simple and effective attrac-
tor state for models of working-memory maintenance
(Wang, 2001). Persistent activity can also be measured with
noninvasive human imaging (Curtis & D’Esposito, 2003) and
neurophysiological (Vogel & Machizawa, 2004) methods.
The strength of delay activity correlates with working-
memory performance variables (e.g., Adam, Robison, &
Vogel, 2018; Rypma, Berger, & D’Esposito, 2002).
However, there are also bothersome niggles. I first
remember focusing on these with Mark Stokes when we
confronted how a stimulus presented during the
working-memory delay impaired the contralateral-delay
activity marker of sustained activity in ERPs in an ongoing
experiment. The observation cast our mind to how single-
unit delay activity in sensory areas is interrupted by inter-
vening stimuli (Miller, Erickson, & Desimone, 1996) and
how sustained firing in the pFC is interrupted in dual-task
contexts (e.g., Watanabe & Funahashi, 2014). Examining
the time courses of neuronal activity in various papers also
revealed a more dynamic picture (e.g., Hussar & Pasternak,
2010; Fuster & Alexander, 1971). Most of the delayed-
response working-memory tasks also conflated mainte-
nance of the memoranda with anticipation of the probe,
suggesting delay activity could reflect anticipatory attention
instead (see Lewis-Peacock & Postle, 2012; Ikkai & Curtis,
2011; Nobre & Stokes, 2011; Lepsien & Nobre, 2007, for
similar arguments).
If persistent delay activity is not the full answer, what else
could maintain relevant neural traces in readiness for
guiding behavior? One possibility is to return to Hebb’s
more dynamic notion of reverberation in a cell assembly.
Dynamic patterns of activity reverberating across cell assem-
blies within and across brain areas could lead to more com-
plex but still efficient attractor states (Wang, 2021). Changes
in excitability in neuronal populations resulting from mem-
brane kinetics and adaptation could also contribute (see Fitz
et al., 2020). Reviewing the evidence and models at the time,
Stokes (2015) suggested that activity-dependent short-term
synaptic plasticity operating upon a spatiotemporally
dynamic neural system could provide an effective mecha-
nism for preserving patterns of neuronal signals in an
excitable functional state across working-memory delays.
Similar models had been proposed for encoding temporal
associations between events (Buonomano & Maass, 2009;
Karmarkar & Buonomano, 2007).
Persistent Delay Activity
Latent Functional States
The discovery of persistent delay-activity neurons in the
pFC (Goldman-Rakic, 1984; Fuster & Alexander, 1971;
The proposal that short-term synaptic weights silently pre-
serve functional states in neuronal assemblies that serve
Nobre
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working memory was simple and elegant, but it also gen-
erated controversy and dilemmas. Controversies consid-
ered the undeniable existence of delay activity and what
specific role it served, as well as the plausibility and evi-
dence for the necessary short-term plasticity. Questions
also resurfaced about whether the mere existence of latent
functional states counts as working memory and about the
appropriate definitions for short-term versus working
memory.
The possibility of information coding in latent func-
tional states additionally provoked the interesting ques-
tion of how scientific knowledge is distorted by what we
can and cannot easily measure. Stokes’ group borrowed
tricks from sonar technology to “ping” latent states with
a high-contrast neutral visual stimulus (Wolff, Ding, Myers,
& Stokes, 2015). The random stimulation interacted with
the pattern of stronger synaptic weights associated with
the recently encoded item, reactivating the latent state,
even if the item was not currently in the focus of attention.
Other studies showed that the availability of latent states
for pinging with visual or transcranial stimulation
depended on the continued task relevance of the memo-
randum ( Wolff, Jochim, Akyürek, & Stokes, 2017; Rose
et al., 2016). In addition, cross-temporal decoding analyses
revealed working-memory states to be dynamic ( Wolff
et al., 2015, 2017).
The attractive silent-working-memory proposal and the
intriguing pinging results raise many interesting questions.
Experimental evidence for silent, latent representations
derives mainly from pinging studies. However, it is impor-
tant to remember that the stimulation used for pinging can
equally well interact with active representations involving
neuronal spiking. Therefore, decoding after pinging is not
direct evidence for a representation being silent.
Silent working-memory representations depend on
synaptic plasticity reinforcing a state within a dynamic sys-
tem. But what are the implications of finding dynamic pat-
terns of activity for arbitrating between silent versus active
neuronal states? The presentation of a visual stimulus nec-
essarily triggers an initial dynamic cascade of sensory pro-
cessing early in encoding. After that, spiking activity could
continue to support working memory either by settling
into a steady state or by continuing to unfold in a more
dynamic way within circuits and networks. Therefore,
finding dynamic patterns of activity is not enough for
concluding that activity-silent mechanisms are sufficient
for supporting working memory.
Furthermore, if plasticity is sufficient, what point(s) in
time should be reinforced? Would a sequential string of
states be reinforced and temporally linked? If not, are
there salient or privileged time points? Understanding
the role of plasticity in temporally evolving brain signals
within extended experience in changing environments
can be daunting. Methods for pinpointing the timing of
cellular events that contribute to relevant states and
computations should prove particularly informative (see
Day-Cooney, Cone, & Maunsell, 2022).
One consequence of encoding information through
short-term plasticity in a dynamical system is that the asso-
ciations are vulnerable to disruption from intervening
events, which themselves may stamp in new associations.
How, therefore, do such models circumvent or overcome
interference to deliver adequate working-memory perfor-
mance in dual-task or multi-stimuli contexts? Would the
strong neutral pinging stimulus itself not be expected to
disrupt the functional state of associations? In other words,
is there a measurable observer effect of pinging a silent
state, that is, a disturbance of the representation by the
act of observation? Recovering activity following interven-
ing stimulation was one of the motivations for developing
an alternative model to persistent delay activity, but it is
not clear how plasticity naturally overcomes the limitation.
The differential decodability between potentially rele-
vant versus irrelevant content in working memory is also
puzzling. Pinging studies have shown that it is possible to
reactivate the contents of currently unattended items in
working memory ( Wolff et al., 2017; Rose et al., 2016).
However, pinging is only effective when the unattended
item may still become relevant for subsequent task perfor-
mance. Pinging leads to significant decoding of unat-
tended items after the first of two retrocues, when the
unattended item may still become relevant for a subse-
quent phase of the trial. However, pinging is ineffective
after the final retrocue, which renders the unattended
item fully irrelevant (Fulvio & Postle, 2020; Wolff et al.,
2017; Rose et al., 2016). It is not obvious how changing
item relevance based on task goals can selectively disrupt
some but not other latent functional states. Is an active
process suggested to flush out the functional connectivity
state associated with the irrelevant items? How would an
active control process interact with the patterns of synap-
tic weights related to different contents within a dynamic
system?
Answers to some of these questions may rest in the
transformations of neural states, but what exactly is meant
by this and how transformations occur are underspecified.
For example, focusing on an item in working memory has
been proposed to engage an output-gating process, plac-
ing the attended content in a state of action readiness
(Muhle-Karbe, Myers, & Stokes, 2021; Panichello & Buschman,
2021; van Ede & Nobre, 2021; Myers et al., 2017; Chatham
& Badre, 2015). In addition, items with similar sensory
content have been proposed to occupy different states
depending on their associated task demands (Nobre &
Stokes, 2019). Multivariate and state-space analyses facili-
tate testing such proposals, but the neurophysiological
underpinnings remain elusive. Does information preserva-
tion in different states rely mainly on changing the involve-
ment of brain areas or the pattern of synaptic changes
within regions? (How) Do rhythmic events at different fre-
quencies implicated in working memory play into the pro-
cess (e.g., Miller, Lundqvist, & Bastos, 2018)? Addressing
these questions will undoubtedly benefit from improving
methods in animal models to measure rapid shifts in brain
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states and functional connectivity (e.g., Perrenoud et al.,
2022; Benisty et al., 2021; Marshall et al., 2021).
Short-term changes in synaptic plasticity are likely to
exist and contribute to the storage and accessibility of sig-
nals deriving from recent experience. However, they may
not provide a full account of working-memory mainte-
nance. Perhaps the most valuable contribution of the pro-
posal is to rouse researchers out of a complacent state of
not questioning the standard paradigm. This new alertness
to other possibilities must now endure, so that we explore
all options with an open mind and additionally consider
how different processes (e.g., tonic sustained activity,
plasticity, adaptation, dynamical reverberation) may come
together and interact to support working-memory encod-
ing, maintenance, prioritization, and selection.
FUTURE DIRECTIONS
Refining Methods
A fuller understanding of working memory will benefit
from the relentless breakthroughs in measurement and
analysis methods in human neuroscience plus from the
ability to relate the research on humans to that in model
systems.
Advances in human neuroscience methods have trans-
formed the research agenda (see Nobre & van Ede,
2020). The sensitivity and spatiotemporal acuity of brain
imaging and neurophysiological methods have increased
continuously. Measures have shifted from group averages
to single trials. Analyses have progressed from univariate
variables related to the amount and timing of information
processing to multivariate variables that also tap into infor-
mation content and quality. Neurophysiological studies can
examine activity across the frequency spectrum, with data-
driven methods to separate frequencies that preserve the
morphology and phase of physiological signals (Quinn,
Lopes-dos-Santos, Dupret, Nobre, & Woolrich, 2021).
Large-scale functional networks can be studied in terms of
their activity strength, connectivity, and dynamics (Quinn
et al., 2019; Brookes et al., 2011). Statistics have evolved
to include Bayesian approaches. Methods borrowed from
other disciplines help analyze and compare structures,
dynamics, and trajectories of functional networks. Compu-
tational models have increased their sophistication and
physiological validity (e.g., Langdon & Engel, 2022).
Human neuroscience researchers should continue to
exercise creativity and ingenuity to push the boundaries
of what we can investigate. Developing the ping method
is a great example ( Wolff et al., 2015). Other possibilities
should be pursued. For example, could invisible visual tag-
ging (Zhigalov, Herring, Herpers, Bergmann, & Jensen,
2019) include stochastic stimulation to yield information
about the timing of noninvasive neural markers causally
influencing performance (see Day-Cooney et al., 2022)?
Importantly, we should enhance and upgrade methods
for capturing behavioral signals. While skill and cleverness
have gone into measuring and analyzing brain activity, the
rich repertoire of behavioral activity has been underappre-
ciated (Nobre & van Ede, 2020). For example, continuous
recordings of pupil diameter and microsaccades have
started yielding new insights into the dynamics of atten-
tion within working memory (van Ede et al., 2020; van
Ede, Chekroud, & Nobre, 2019; Zokaei, Nour, et al.,
2019). Continuous measures of motor excitability and spa-
tiotemporal aspects of response trajectories are similarly
promising (Echeverria-Altuna et al., 2022; Dotan,
Pinheiro-Chagas, Al Roumi, & Deheane, 2019; Novembre
et al., 2019). Rather than discard external behavioral
markers correlated with psychological functions (Quax,
Dijkstra, van Staveren, Bosch, & van Gerven, 2019; Mostert
et al., 2018), we should embrace their information value to
unlock new questions.
However, characterizing specific dynamical circuits in
the human brain may remain out of reach for current or
forthcoming noninvasive methods, either for defining
computations within microcircuits or in networks across
brain areas. The problem is challenging for tracking neu-
ronal firing or extracellular field potentials, and the chal-
lenge is magnified manifold for revealing latent functional
states in circuits or networks. Methods development in
animal models increasingly allows the individuation and
manipulation of specific circuits and networks. For exam-
ple, mini-scope calcium imaging in rodents can reveal acti-
vation and changes in neuronal ensembles resulting from
experimental manipulations (Mau et al., 2022). Optoge-
netic methods enable the targeting of neuronal subpop-
ulations, and methods for genetically sequencing and
classifying neurons are developing at a staggering pace
(e.g., BICCN, 2021; Krienen et al., 2020). Exciting innova-
tive optogenetic protocols using random (white-noise)
trains of stimulation are opening the doors for investigat-
ing the timings of critical neuronal contributions to per-
formance in tasks (Day-Cooney et al., 2022).
Integrating Research Camps
Researchers studying working memory in humans and ani-
mal models will need to work together for real progress.
Task developments and whole-brain measurements in
humans can expose principles of psychological and neural
organization to frame in-depth investigations at the net-
work, circuit, and cellular levels. In turn, mechanistic dis-
coveries can prompt the search for related markers in
noninvasive measurements and test for their functional
contributions. We are far from a coordinated collaboration
in working-memory research. Alignment of work in
humans and nonhuman primates is better, although the
conceptual and task advances from human research have
been slow to percolate into non-human-primate research,
for example, on the flexible attention functions operating
in working memory (but see Panichello & Buschman,
2021; van Ede & Nobre, 2021). In rodents, the very term
working memory carries different connotations and tasks
Nobre
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employed typically diverge substantially from those in
primates.
Virtual reality provides a promising opportunity for
cross-linking research across species while also upgrad-
ing the ecological validity of experimental tasks. The
methodology allows for capturing varied continuous
behavioral and brain measures with strong experimental
control in naturalistic immersive settings (Draschkow,
2022). Virtual reality is already widely used in rodent
studies to great effect. Working-memory studies are just
starting in humans and already changing our understand-
ing of natural working memory (Draschkow, Kallmayer,
& Nobre, 2021). A propitious way forward would see
the development of closely matched experimental tasks
across species, although recognizing that no task can be
fully equated given their different evolutionary adapta-
tions and the training methods required. Analytical tools
that can describe functional states, relationships, and
dynamics in data at various scales may help interrelate
findings.
Work within species, including humans, also needs to
proceed in a better-integrated way. Focusing on certain
brain markers is a natural tendency, but this seeds differ-
ent strands of research that can be difficult to reconcile.
In human visual working memory, for example, different
sets of studies concentrate on developmental trajecto-
ries; neuropsychological dissociations; and various types
of neural measures: sustained activity, theta oscillations
and cross-frequency coupling, alpha oscillations, decod-
ing of contents, and dynamics in state-space. Are the var-
ious lines of findings compatible? Are there systematic
differences in the types of tasks associated with different
measurement types? Can we build cohesive models that
harmonize the various strands to guide future research?
It is time to remove the blinders and pull groups
together to discuss points of tension and how to resolve
them.
Rifts between research subfields are even deeper and
can be similarly counterproductive. Fault lines follow folk
psychological concepts—attention, memory, decision-
making, action, language, planning, and so forth. In reality,
these concepts are highly interrelated. One day, we may
reach a very different description of the natural kinds
supporting cognition (Churchland, 1981). For now,
researchers stand to gain from considering concepts and
experimental approaches across the textbook psychologi-
cal domains. Working memory is a special point in case. As
a bridge between sensory experience and adaptive behav-
ior, working memory should be considered from an evo-
lutionary and ecological perspective (Cisek, 2019) and not
just for its phenomenological qualities. Working memory
is an essential building block for orienting attention,
decision-making, long-term memory formation, action
control, language, planning, and so forth. As such, empha-
sis is increasingly falling on the functional and pragmatic
aspects of working memory (van Ede & Nobre, 2023;
Nobre & Stokes, 2019).
When integrating findings and perspectives across spe-
cies, strands from different experimental methods, and
psychological domains, it is also important to preserve rel-
evant distinctions. Early neuropsychological studies
revealed many important dissociations between psycho-
logical functions. Some of the dissociations may have been
misleading, such as dividing short-term and long-term
memory based on performance in tasks with disparate
materials and demands. Some of the dissociations, how-
ever, may still stand, such as HM’s ability to maintain online
behavior that relies on working memory without the ability
to commit those contents to long-term memories. By their
nature, neuropsychological milestones become associated
with specific brain areas. In the case of memory, the hippo-
campus and medial temporal area became the territory
dedicated to episodic long-term memory, characterized
by contextual and relational associations. Increasingly,
studies indicate the involvement of these areas in working
memory and perception, depending on the type of content
required for task performance (e.g., Graham et al., 2010).
Thus, alternative models of hippocampal function are
developing (e.g., Barry & Maguire, 2019; Maguire & Mullally,
2013). These advances, however, do not negate the impor-
tant distinction between perception, working memory,
and long-term memory. The challenge ahead is to refine
the understanding of how brain areas contribute to differ-
ent extents and in different ways to larger-scale networks
supporting psychological functions and to uncover the
critical structural, activity, plasticity, connectivity, and
computational parameters for performing natural tasks
based on different types of content. Developing tasks that
equate stimulus materials and task demands for assaying
performance based on perception, working memory,
and long-term memory should prove particularly fruitful
(e.g., Richter, Cooper, Bays, & Simons, 2016).
Another fundamental distinction to preserve, at least in
human studies, is the obvious separation between the
mental (subjective) and neural (implementational) levels
of description. Most of us, as reductionists, believe in a
mapping between the neural and the mental. However,
centuries of musing, theorizing, and experimenting rule
out simple one-to-one isomorphic mappings. In the case
of working memory, the subjective experience of appre-
hending the rich gamut of sensory experience contrasts
sharply with the limited capacity to report memoranda
objectively. Most working-memory studies focus primarily
on objective behavioral measures, leaving subjective phe-
nomenology untapped. We study humans as what philos-
ophers describe as zombies (Chalmers, 1996; Kirk, 1974).
Yet, conflating or flip-flopping between these levels is a
frequent bad habit when generating hypotheses and inter-
preting findings. Holding something fixed in mind does
not imply a sustained fixed neural mechanism. Conversely,
many consciously inaccessible short-term traces of experi-
ence impact behavior. Stitching across this ultimate divide
requires a careful and critical appreciation of their concep-
tual distinctions.
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Conclusions
Recent research findings and theoretical proposals have
disrupted the relatively calm field of working memory,
highlighting just how enigmatic this fundamental building
block of adaptive cognition remains. We are not “there
yet,” so we should maintain the level of inquisitive alert-
ness toward the new standard views that are forming.
Fuller understanding may require conciliating and inte-
grating multiple perspectives that still seem antagonistic.
Breaking some scientific bad habits could help set the field
on a more productive path.
Agreeing on a core nomenclature would be transforma-
tive. For example, is it enough to consider the traces of
recent experience for guiding behavior as working mem-
ory or is active manipulation required? What functions
would count? Could terminology usefully differentiate
the neural and subjective states? Short of a shared taxon-
omy, defining terms would be a start. Keywords carry dif-
ferent connotations in the working-memory field and vary
even more between neighboring fields. Papers rarely spell
out definitions and premises, causing readers to project
their own intellectual biases, which may distort the
intended messages.
Stepping outside the comfort zone will be essential. Dia-
logue between divergent and even opposing camps
should help distil what notions are mutually incompatible
or reconcilable, develop falsifiable models, and inspire piv-
otal experimental hypotheses. Scientific niches have
formed across many domains, such as species of study,
methods, methodological markers, level of analysis, and
experimental tasks. Small communities thus indepen-
dently develop repertoires of findings, ideas, and termi-
nology in parallel, with few attempts to build bridges.
Workshops and symposia in larger conferences often con-
vene like-minded scientists. Instead, intensive meetings
and sessions exploring points of disconnection and ten-
sion within a safe environment might prove much more
instructive and possibly even more enjoyable.
Finally, as scientists, we are trained to “pontificate.” Our
presentations and writings carefully synthesize what we
have learned and understood, preferably in a cohesive
and engaging narrative. However, we also need to find a
voice for our complementary and equally important igno-
rance. Ignorance, after all, is the true engine of science
(Firestein, 2012). Openly admitting ignorance is not a nat-
ural or comfortable activity. Those lingering confusions,
nagging doubts, methodological preoccupations, and
unyielding questions are often saved for solitary contem-
plation. Sometimes though, you find a kindred spirit in a
close colleague with whom you can share and learn from
ignorance. Frank, broad, probing, meandering conversa-
tions then turn into creative fuel and intellectual advance-
ment. I am lucky to count on some close mind-opening
colleagues, including Mark Stokes. My hunch is that find-
ing a way to open similar exchanges more widely would
turbocharge our science and improve us as scientists.
Meanwhile, I remain open-minded and will be curious
to look back in another decade to check how our under-
standing of working memory has progressed. Who knows?
By then, we may have done away with working memory as
a separate psychological domain altogether, and rather
consider it as an intrinsic memory function embedded
within other psychological domains that evolved to sup-
port adaptive behavior. But, whether an independent
domain or a ubiquitous property of neural systems, work-
ing memory will always be that remarkable bridge between
the past and the future essential for so much intelligent
and flexible human behavior.
Acknowledgments
The ideas in this reflection benefitted from many conversations
and exchanges with current and past members and collabora-
tors of the Brain & Cognition Lab, especially Freek van Ede,
Nahid Zokaei, Sage Boettcher, and Dejan Draschkow in recent
years. Our working-memory research is supported by a Well-
come Trust Senior Investigator Award (104571/Z/14/Z), a James
S. McDonnell Foundation Understanding Human Cognition
Collaborative Award (220020448), and the NIHR Oxford Health
Biomedical Research Centre. The Wellcome Centre for Inte-
grative Neuroimaging is supported by core funding from the
Wellcome Trust (203139/ Z/16/ Z). For the purpose of open
access, the author has applied a CC BY public copyright license
to any Author Accepted Manuscript version arising from this
submission.
My thanks go to Brad Postle for suggesting this contribution and
to Brad Postle, Freek van Ede, Nahid Zokaei, Dejan Draschkow,
Dongyu Gong, and Irene Echeverria-Altuna for helpful comments
on a previous draft of the manuscript.
Reprint requests should be sent to Anna C. Nobre, Department
of Experimental Psychology, University of Oxford and Oxford
Centre for Human Brain Activity, Wellcome Centre for Integra-
tive Neuroimaging, Department of Psychiatry, University of
Oxford, South Parks Road, Oxford, UK, OX1 3UD, or via e-mail:
kia.nobre@ohba.ox.ac.uk.
Funding Information
James S. McDonnell Foundation (https://dx.doi.org/10
.13039/100000913), grant number: 220020448. Wellcome
Trust (https://dx.doi.org/10.13039/100010269), grant
number: 104571/Z/14/Z. Wellcome Trust (https://dx.doi
.org/10.13039/100010269), grant number: 203139/Z/16/Z.
Diversity in Citation Practices
Retrospective analysis of the citations in every article pub-
lished in this journal from 2010 to 2021 reveals a persistent
pattern of gender imbalance: Although the proportions of
authorship teams (categorized by estimated gender iden-
tification of first author/ last author) publishing in the
Journal of Cognitive Neuroscience ( JoCN ) during this
period were M(an)/M = .407, W(oman)/M = .32, M/ W =
.115, and W/ W = .159, the comparable proportions for
the articles that these authorship teams cited were
M/M = .549, W/M = .257, M/ W = .109, and W/ W = .085
Nobre
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(Postle and Fulvio, JoCN, 34:1, pp. 1–3). Consequently,
JoCN encourages all authors to consider gender balance
explicitly when selecting which articles to cite and gives
them the opportunity to report their article’s gender
citation balance. The author of this article reports its pro-
portions of citations by gender category to be as follows:
M/M = .539; W/M = .211; M/ W = .211; W/ W = .039.
Note
1. By paradigm, I mean “a conceptual or methodological
model underlying the theories and practices of a science or dis-
cipline at a particular time; (hence) a generally accepted world
view” (Oxford English Dictionary). This word is often used,
incorrectly in my view, to refer to an instance of an experimen-
tal design or setup.
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