Attention and Memory
Brynn E. Sherman1 (brynn.sherman@yale.edu)
Nicholas B. Turk-Browne1, 2 (nicholas.turk-browne@yale.edu)
1
Department of Psychology; 2Wu Tsai Institute, Yale University
To appear in: Oxford Handbook of Human Memory. M.J. Kahana & A.D. Wagner (Eds). Oxford
University Press.
Abstract
Attention plays an important and pervasive role in human perception, filtering complex sensory
input based on what is most salient or relevant, and gating the entry of this information into
conscious awareness. Beyond modulating processing of the current environment, the control of
attention over perception has downstream consequences for processing of the past and future,
determining what information is available for memory encoding and retrieval. This interaction
between attention and memory is bidirectional, as goals, episodes, and knowledge held in
memory in turn inform attentional selection and vigilance. These bidirectional interactions can
be found across different types of memory and their underlying brain systems. In this chapter, we
catalog the ways in which attention and memory interact, focusing on working memory, episodic
memory, and learning. By reviewing the neural and behavioral mechanisms underlying these
interactions, we reconceptualize attention as serving a broader role in the organization of the
mind, and memory as being fundamentally selective and interwoven with perception.
Keywords: cuing, selection, modulation, encoding, retrieval, hippocampus
Introduction
Various factors influence memory encoding and retrieval, including context, semantics,
emotion, and motivation — all of which and more are covered in the chapters of this book.
However, one of the strongest predictors of memory, and the focus of this chapter, is attention.
The term attention has many different meanings, but here we refer to how salience or goals
prioritize a subset of external inputs or internal states (Chun et al., 2011). This attentional
selection in turn leads to modulation — enhancing or facilitating processing of the targets of
attention and suppressing or inhibiting processing of distracting information.
By this definition, attention interacts with memory in several ways. Attention has a
powerful influence over what we perceive and thus necessarily influences what can be encoded
into memory (Uncapher et al., 2011). When oriented internally, attention helps select among
representations to be retrieved from memory (Chun & Johnson, 2011). Attentional priorities
themselves are often derived from memory, informed by when and where relevant information
was found in recent or long-past experiences (Hutchinson & Turk-Browne, 2012). The
interactions between attention and memory are thus bidirectional and recurrent, with attention
influencing which memories are formed, these memories guiding subsequent attention, and this
in turn impacting new encoding and retrieval.
Here we review evidence for interactions between attention and memory in behavior and
the brain, with an emphasis on how attentional selection operates during encoding and retrieval,
and how retrieval influences attentional selection. We highlight effects across a range of brain
systems for attention and memory (Figure 1), from shorter-term working memory, to longer-term
episodic memory, to more gradual forms of statistical learning. We consider both domaingeneral and specialized mechanisms, for example: evaluating whether attention and working
memory reflect a single flexible cognitive process versus two distinct processes, and dissociating
influences of attention on long-term memory that are a byproduct of modulation of sensory
systems versus a result of direct modulation of memory systems. Exploring this broad literature
leads to a comprehensive understanding of how attention and memory coordinate to support
cognition.
Figure 1. Summary of brain systems involved in attention and memory that are discussed
in this chapter. WM: working memory; LTM: long-term memory. The color shading
corresponds to the location of specific brain systems (purple: sensory visual cortices; blue:
frontoparietal networks; green: hippocampus; pink: neuromodulation stemming from
midbrain structures). Dashed lines indicate subcortical structures.
Attention and working memory
Attention is often thought of in terms of the external world — how we attend to the road
while driving or the professor giving a lecture. Yet we can also attend to our internal states. For
example, mind-wandering during a lecture involves orienting attention to unrelated thoughts in
mind rather than to the content of the lecture. A key insight in understanding the role of attention
in memory is that attention can be allocated to the contents of our memories, even when they are
not directly supported by stimuli in the current environment (Chun et al., 2011). This process of
internal attention dovetails with a conventional definition of working memory: the process by
which absent stimuli are actively held or maintained in mind (see Chapters 2.9 and 2.10). Indeed,
capacity limits in working memory are thought to emerge from the limitations of attentional
selection (Cowan, 1998; Cowan, 1999; Garavan, 1998; Oberauer, 2002). To what extent, though,
is internally oriented attention responsible for maintaining information in working memory?
Attention can be allocated to the contents of working memory
Traditional views of working memory suggest that maintenance is supported by two
modality-specific subsystems: a visuospatial sketchpad for holding and manipulating images in
mind, and a phonological loop for rehearsing auditory information (Baddeley, 1992). Although
these systems accord with intuitions about what it feels like to hold something in working
memory, subsequent research suggested that maintenance — particularly in the visuospatial
domain — can be understood as an allocation of attention. In a seminal behavioral study,
participants responded faster to an external stimulus presented at the same location as an item
held in working memory, and working memory accuracy was lower when external attention was
drawn away from a to-be-remembered location (Awh et al., 1998). These findings suggest that
working memory invokes attention, resulting in enhanced processing for locations currently held
in mind.
Attention can also be used to select among the contents of working memory. This has
been studied using the retro-cuing paradigm, in which participants are cued during a delay period
to one or more of several stimuli that are no longer physically present but are being held in
memory. Retro-cues provide similar performance benefits as pre-cues, resulting in enhanced
memory for retro-cued objects (Griffin & Nobre, 2003; Makovski & Jiang, 2007) and features
(Niklaus et al., 2017). This selection during working memory maintenance may boost memory
by protecting target items from delay-related degradation, rather than prioritizing the item for
retrieval. This interpretation is supported by the fact that switching the retro-cue (i.e., asking
participants to shift attention to previously unattended memory contents) does not provide any
benefit to memory, presumably because those contents have degraded since the original cue
(Matsukura et al., 2007). Indeed, information held outside the focus of attention in working
memory is remembered with less precision (LaRocque et al., 2015). Nevertheless, attention can
be allocated dynamically and flexibly over time during maintenance, including by relying on
temporal statistics to prioritize information likely to be probed at different timepoints (van Ede et
al., 2017). Taken together, these data suggest that attention plays an important role in keeping
representations active.
Neuroimaging studies provide further evidence for this role of attention in working
memory. Maintaining a spatial location in memory evokes similar activity patterns in visual
cortex as when attending to that location in external space (Awh & Jonides, 2001). This holds for
features such as orientation as well (Harrison & Tong, 2009; Serences et al., 2009). The fidelity
with which remembered information can be decoded in these cortical regions predicts the
precision of behavioral reports of these memories (Emrich et al., 2013). Furthermore, this
mirroring of attention and memory persists along the visual hierarchy: orienting attention to
maintained faces or scenes evokes patterns of activity in visual cortex analogous to external
visual attention to those categories (Lepsien & Nobre, 2006; Lepsien et al., 2011). Beyond
modulating sensory cortex, attention to perceived stimuli and attention to remembered stimuli
recruit a similar broad network of brain regions (e.g., Nobre et al., 2004; Lepsien et al., 2005;
Lepsien et al., 2011; see Corbetta & Shulman, 2002; Xu, 2017, 2018). These data suggest that
attention may act on working memory representations similarly to how it acts on perceptual
representations. Causal interventions using transcranial magnetic stimulation (TMS) further
support this view. TMS of both sensory cortex (Zokaei et al., 2014) and higher-order attentional
regions (Rose et al., 2016) can increase memory for an unattended stimulus. Stimulation in these
cases is thought to mimic attention, thus enhancing perceptual processing of the unattended
stimulus and rescuing memory degradation.
Common neural correlates between attention and working memory have been used to
argue that these processes are intricately related, if not two sides of the same coin. Yet, such
findings leave open important questions about the nature of the representations being measured:
To what extent does neural activity during a working memory delay reflect the contents of
attention, as opposed to the contents of memory (over which attention can operate)? In other
words, is it possible to actively maintain information (the hallmark of working memory) without
internally attending to it?
Converging studies utilizing fMRI (Lewis-Peacock et al., 2012) and EEG (LaRocque et
al., 2013) attempted to de-confound attention and memory, and found that delay period activity
reflects the contents of attention rather than memory more generally. When presented with
distractors during a delay period, patterns of brain activity contained information about the
category of the distractor image (which presumably had captured attention), rather than the
category of the to-be-remembered target image (Lewis-Peacock et al., 2012). Critically,
behavioral performance indicated that the target was still held in working memory, despite not
being decodable during the delay period. Relatedly, adding a distractor task or intervening items
during the delay period disrupts maintenance signals in visual cortex without impairing behavior
(Miller et al., 1993; Miller & Desimone, 1994; Miller et al., 1996; Bettencourt & Xu, 2015).
These findings reveal that neural representations thought to be related to working
memory per se may in fact reflect the allocation of attention, either internally (to memories) or
externally (to distractor stimuli). Of course, working memory can survive distraction, suggesting
that while visual areas may reflect the contents of attention rather than memory, other brain
regions that exhibit persistent delay period activity, such as prefrontal and posterior parietal
cortices (see Xu, 2017; Myers et al., 2017), may represent the contents of memory more
faithfully. These regions may better track working memory performance in the face of distraction
and may coordinate memory-related activity in other cortical regions, including sensory systems,
through top-down control.
Working memory influences attentional selection
The above section discussed how attention can be allocated in working memory, but the
reverse may also be true. Indeed, the contents of working memory can drive the allocation of
attention. Theories of attention posit that allocating attention voluntarily requires holding a target
in mind (see Desimone & Duncan, 1995), a process that fundamentally relies on working
memory. This endogenous or goal-directed attention differs from exogenous or stimulus-driven
control, in which attention is captured in an obligatory way by salient, bottom-up information,
such as abrupt onsets or stimulus changes (Chun et al., 2011). The link between endogenous
attention and working memory has been demonstrated by studies of task interference. When
working memory is placed under load, performance on a concurrent attention task suffers from
increased distractor interference (de Fockert et al., 2001). This is thought to reflect a diminished
ability to hold the attentional target in mind when working memory is otherwise consumed with
task-irrelevant information.
Working memory determines not only whether attention can be allocated, but also how it
is allocated. That is, even in cases when there is no direct competition between working memory
and attention, the contents of working memory can bias attention. This biasing can occur in
space: responses to an attention probe are faster when that probe is presented on the same side of
the screen as an object being held in working memory (Downing, 2000). The biasing can also
occur over features: visual search is facilitated when the search target matches the feature(s) of
an item being held in working memory (Soto et al., 2005). Subsequent work examined whether
this biasing of attention by memory is a consequence of the task design and explicit strategy, or
whether memory signals can automatically guide attention. Consistent with an automatic effect,
items held in memory facilitate search for a matching target even when such matches are
statistically improbable (Carlisle & Woodman, 2011), and even when other salient cues that
capture exogenous attention are present (Soto et al., 2006).
Interactions between working memory and attention can also be revealed by examining
how working memory influences physiological responses related to perception and attention. For
example, pupil size can be modulated by working memory demands (Unsworth & Robison,
2016b; Zokaei et al., 2019). Capitalizing on the fact that the pupil dilates to low light levels and
constricts to higher light levels, pupil response can be used as an index of what information is
being attended to in working memory (Figure 2A-B). Specifically, while maintaining an
orientation in working memory, the pupil is modulated by the brightness of the oriented grating
at encoding, despite the fact that the brightness is irrelevant to the memory task and will not be
probed at test. Further, pupil size provides a dynamic, online signature, as it changes when
attention is switched between items in working memory (Zokaei et al., 2019).
Working memory can also influence attentional guidance via direct influences on the
oculomotor system (Figure 2C-D). For example, when asked to report the color of a stimulus
from working memory, gaze is directed toward the spatial location in which the stimulus had
appeared (van Ede et al., 2019). These cases provide compelling evidence that working memory
can powerfully influence attentional selection. However, whether and how these pupillary and
oculomotor responses affect subsequent attentional, perceptual, and mnemonic processing
remains unstudied.
A
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Figure 2. Working memory modulates physiological responses implicated in visual
attention. (A) Participants were shown two Gabor patches of different illumination and
different orientations. During the delay period, they were auditorily cued that one of the
patches would be probed for orientation report. (B) Pupil size during the delay period was
modulated by the task-irrelevant luminance of the to-be-reported item. For example, when
cued that the darker patch’s orientation would be probed, the size of the pupil was larger.
Adapted with permission from Zokaei et al. (2019). (C) Participants were shown two
oriented lines of different colors. At test, they were cued with the color of one of the lines,
indicating which orientation to report. (D) Gaze was biased towards the location of the tobe-reported item. Adapted with permission from van Ede et al. (2019).
Working memory as internal attention
In addition to bidirectional behavioral influences and shared neural substrates, working
memory and attention have also been tightly linked at the trait level (see Engle, 2002) and may
share cognitive resources (see Chun, 2011). Indeed, individuals with greater working memory
capacity perform better on attentional tasks (Kane et al., 2001), and attentional ability accounts
for a significant proportion of the variance in working memory capacity (Unsworth & Spillers,
2010; Unsworth et al., 2014). Low working memory capacity individuals demonstrate both more
interference from irrelevant distraction (Fukuda & Vogel, 2009; Fukuda & Vogel, 2011) and
more attentional lapses (Unsworth & Robison, 2016a). Attention and working memory also covary within an individual over time. Fluctuations in attentional control (Adam et al., 2015) and
attention lapses (Unsworth & Robison, 2016a) predict working memory performance on a trialby-trial basis.
Although these data implicate attentional mechanisms in working memory success, a
recent study with real-time manipulation and analysis of attention provided more direct evidence
(deBettencourt et al., 2019). Participants completed a sustained attention task in which colored
shapes appeared on the screen and participants reported the shape. The distribution of the shapes
over time was skewed such that certain shapes had a much higher probability than others, and
thus attentional vigilance was required to not miss rare shapes. Some trials were followed by a
working memory probe, in which participants reported the color at each location in an array.
This dual-task design allowed for trial-specific measures of attention (response time and
accuracy to classify the shape) and working memory (proportion correct on the probe). After
confirming a trial-by-trial correlation between these measures, the researchers sought a more
causal link. Specifically, they assessed attentional fluctuations in real-time by comparing the
response time (RT) from the most recent three sustained attention trials to the distribution of RTs
throughout the task. Memory probes were presented when the trailing window RT was either
faster than a threshold, related to attentional lapses (i.e., reduced vigilance/habitual responding),
or slower, as a control for better attention. Consistent with lapses of attention causing working
memory errors, performance was lower for memory probes triggered after a fast vs. slow trailing
RT, with fewer items stored and thus lower capacity.
Further evidence of shared resources comes from studies demonstrating competition
between attention and working memory. For example, when spatial information is held in
working memory, attention-demanding visual search is slowed (Woodman & Luck, 2004).
Similarly, increasing working memory load leads to more interference from distractors in an
attention task (de Fockert et al., 2001; Lavie et al., 2004). Together, these findings suggest that
increasing demand on working memory leaves fewer cognitive resources for effortful aspects of
attention, such as filtering out distraction or staying on task. Interestingly, such competition may
be modality-specific: loading up working memory with non-overlapping spatial information does
not impair visual search (Woodman et al., 2001), and interference is reduced when distractors are
more similar to the items in working memory (Park et al., 2007). The specificity of these
competitive effects suggests that the resources being shared are not generic (e.g., motivation,
control), but rather tied more closely to the sensory systems and constraints at play. Notably,
such results support the concept that there may be discrete working memory subsystems even
within the visual domain (Smith et al., 1995; Hakim et al., 2019), contrasting with prior work
that conceptualized two discrete, sensory-specific subsystems: one responsible for both visual
and spatial information (visuospatial sketchpad) and one for verbal information (phonological
loop; see Baddeley, 1992).
Although questions remain about the format of working memory, the body of work
demonstrating interactions between visual attention and visual memory has led to the suggestion
that working memory may in fact be a form of internal attention rather than a separate memory
system (e.g., Awh & Jonides, 2001; Postle, 2006; Chun et al., 2011). The depth of this analogy is
still debated (see Xu, 2017; Myers et al., 2017), but it may be most applicable to forms of
working memory without significant distraction (e.g., visual short-term memory and retro-cuing
tasks), whereas more complex and continuous tasks (e.g., n-back and AX-CPT) may recruit
additional processes and brain regions to prevent distraction. Moreover, internal attention itself
may not be a singular concept, and may instead depend on multiple attentional components, such
as for prioritizing locations and maintaining object information (Hakim et al., 2019).
This latter multi-component view of internal attention highlights an interesting fact about
the working memory literature: most of the studies described throughout this section focused on
the maintenance of either spatial locations or objects/features, but generally not both (c.f., Rao et
al., 1997; Postle & D’Esposito, 1999). Natural settings often demand that we integrate these
sources of information, in order to keep track of objects in particular locations. Given that such
integration has long been fundamental to theories of attention, as in the role of spatial maps in
feature integration theory (e.g., Treisman & Gelade, 1980), a fruitful avenue for further exploring
links between attention and working memory may be to address more configural, multidimensional forms of working memory.
Attention and long-term memory
Attention allows us to pursue goals and adapt behavior in the moment, and thus naturally
interacts with working memory as a short-term store of information that may be immediately
relevant. However, attention can also guide, and be guided by, long-term memory (Hutchinson &
Turk-Browne, 2012). To this end, attention helps determine not only which aspects of an
experience are encoded into episodic memory to influence future processing (including future
deployment of attention), but also which retrieval cues are selected, in turn impacting which
episodic memories are retrieved. Here, we describe different conceptions of attention and the
relationship of each to episodic memory, we distinguish effects related to encoding vs. retrieval,
and we consider how these effects manifest from the known neural mechanisms of attention.
Attention during episodic encoding
It may seem intuitive that if you pay attention to something, you are more likely to
remember it later. However, attention is not a singular construct: attention can operate in
different modalities, over objects, features, spatial locations, and points in time (Chun et al.,
2011). Further, as noted earlier, attentional selection is not only endogenous or goal-directed, but
can also be captured by salient environmental cues in an exogenous or stimulus-driven manner.
Thus, understanding how attention interacts with long-term memory requires considering the
possibility that different kinds of attention exhibit different interactions. Throughout this section,
we focus on various ways in which researchers have manipulated the allocation of attention to
memoranda in order to shed light on these complex interactions.
One task manipulation intended to broadly disrupt attention is to require processing of
two or more streams of information simultaneously. If attention is required for memory
encoding, then encoding should be worse under such conditions of divided attention (analogous
to the interference between working memory and attention discussed above). Indeed, across a
range of tasks, dividing attention during encoding impairs later memory performance (Craik et
al., 1996; Fernandes & Moscovitch, 2000; see Craik, 2001). Interestingly, divided attention only
modestly influences judgments of familiarity, whereas it more strongly disrupts recollection
(Gardiner & Parkin, 1990; see Chapter 5.6). Recollection is thought to reveal richer, more
contextual episodic memories that rely upon the hippocampus (see Yonelinas, 2002), suggesting
that attention may play a role in relational binding, rather than merely in memory for constituent
items. On the other hand, effects of divided attention on associative memory have been linked to
disrupted encoding of individual items (Naveh-Benjamin et al., 2003).
Converging evidence that attention influences episodic encoding comes from studies of
selective attention, a different kind of attention manipulation in which participants prioritize only
one of two or more streams of information while ignoring the other(s). For example, in a dichotic
listening paradigm with different auditory inputs in each ear, one of which is attended, only the
contents of the attended ear are later remembered (Moray, 1959). Likewise, when attending to
one category of composite face-scene images, only the image from the attended category is
remembered (Yi & Chun, 2005). Selective attention can also be manipulated on a trial-by-trial
basis with cuing tasks. For example, spatial cues enhance subsequent memory encoding for items
that occur at the cued locations (Uncapher et al., 2011; Turk-Browne et al., 2013). In all of these
cases, the impact of attention on memory may be a side effect of attentional modulation of
perception (Rees et al., 1999; Rees & Lavie, 2001). During a dichotic listening paradigm, for
example, if participants do not perceptually register the contents of the unattended ear, this
information may not persist far enough along the sensory processing hierarchy to be encoded
into memory systems.
Relatedly, selective attention is not all or none, but rather graded based on how deeply
stimuli must be processed to perform a task (Craik & Lockhart, 1972). These different levels of
processing have distinct mnemonic consequences: when oriented to the phonetic features of a
word (e.g., whether it rhymes with another word) vs. to its semantic features, participants
perform worse on a standard recognition memory test but better on a test where the words
rhymed with the encoded words (Morris et al., 1977).
Beyond conceiving of attention as a limited resource that must be divided or a selective
process that enhances relevant information and suppresses irrelevant information, sustaining a
consistent attentional state over long periods of time is itself effortful and tiring. Such vigilance
is also related to episodic memory. For example, lapses in sustained attention — reflected in
faster, habitual responses to stimuli over a period of time — are associated with worse
subsequent memory for those stimuli (deBettencourt et al., 2018).
Our ability to allocate attention is also influenced by the temporal structure of our
experience. Shifts in context, or event boundaries, can redirect attention (Zacks et al., 2007),
perhaps due to changes in low-level visual information (Huff et al., 2012) and autonomic arousal
(Clewett et al., 2020). Such event-based modulation of attention can result in a bias away from
associative processing and toward local details, resulting in enhancement of memory for items
occurring at the boundaries (Boltz, 1992; Gold et al., 2017; Heusser et al., 2018) and decreased
integration across events (Heusser et al., 2018; see Clewett et al., 2019). On the other hand,
promoting associative processing within an event (e.g., by encouraging participants to form links
between successive items) benefits temporal order memory, and shifting attention away from
associative processing (by inserting a distractor task between stimuli) reduces temporal memory
(DuBrow & Davachi, 2013). Nevertheless, even with an explicit associative encoding strategy,
event boundaries disrupt temporal order memory, suggesting that event boundaries may have
shifted attention away from or disrupted associative processing.
The studies above involve attention to neutral, innocuous stimuli, but psychological
factors such as stress and emotion also control attention (Anderson & Phelps, 2001; Öhman et
al., 2001; see Chapters 3.7 and 9.9). Exogenous attentional allocation to emotional stimuli can
enhance memory for the arousing stimulus, often at the cost of memory for surrounding (even if
relevant) details (see Mather & Sutherland, 2011). For instance, attentional capture by a weapon
can enhance memory for the weapon at the cost of memory for the perpetrator (Loftus et al.,
1987). Moreover, while the details of arousing stimuli are better remembered, the association
between arousing and neutral stimuli does not similarly benefit (Mather et al., 2009). Relatedly,
reward can shift attention (see Maunsell, 2004; Awh et al., 2012), perhaps in part explaining the
role of reward in episodic memory (e.g., Adcock et al., 2006; Wolosin et al., 2013; Clewett &
Mather, 2014; Mason et al., 2017).
The behavioral findings discussed above shed light on how differential allocation of
attention can influence memory, but are agnostic as to whether these effects result from feedforward modulation of sensory processing or direct modulation of episodic memory processes.
Understanding the neural mechanisms of these interactions between attention and memory sheds
light on this question.
The biased competition theory of attention is a prominent model of how attention
operates in sensory cortex (Desimone & Duncan, 1995). Visual environments contain multiple
features and objects at different locations, all vying for representation in stimulus-selective visual
areas. Attention to one of these objects modulates the amplitude of the neural response it evokes,
strengthening its representation relative to unattended objects. As these neural signals propagate
forward in the sensory hierarchy, competitive dynamics in each layer amplify this difference
between attended and unattended stimuli, resulting in an increasingly exclusive representation of
attended stimuli (see Kastner & Ungerleider, 2000). This attentional modulation is observed
across levels of the visual hierarchy, including in the cortical areas that directly feed into the
hippocampus (O’Craven et al., 1999; Uncapher & Rugg, 2009; Dudukovic et al., 2011; TurkBrowne et al., 2013).
Such modulation has also been observed in the hippocampus proper (Uncapher & Rugg,
2009; Carr et al., 2013), though not consistently (Yamaguchi et al., 2004), and may be dependent
upon whether attention is drawn to items or relations (Dudukovic et al., 2011; Córdova et al.,
2019). Regardless, hippocampal modulation may reflect a form of biased competition, whereby
attentionally modulated signals that emanate at earlier stages, perhaps in MTL cortex, propagate
downstream. This spreading is consistent with the fact that subsequent memory can be predicted
from attentional modulation in both MTL cortex and the hippocampus (Uncapher & Rugg, 2009;
Carr et al., 2013; Turk-Browne et al., 2013). Biased competition has also been invoked to explain
findings of how emotion influences memory, based on evidence that arousing stimuli are
prioritized in sensory processing, resulting in downstream memory enhancements (Mather &
Sutherland, 2011).
This account also relates to the role of repetition suppression in attention and memory.
Repeated stimuli evoke weaker responses in stimulus-selective sensory cortex relative to novel
stimuli. This disparity can tilt competition toward novel stimuli, enhancing their encoding into
episodic memory (Hutchinson et al., 2016). This would have the advantage of prioritizing the
encoding of novel information in memory. Indeed, repetition suppression for a stimulus has been
linked to both implicit (Maccotta & Buckner, 2004; Wig et al., 2005; Ward et al., 2013) and
explicit (Turk-Browne et al., 2006) memory (see Chapters 2.5 and 5.8). The fact that repetition
suppression only occurs for attended stimuli (Eger et al., 2004; Yi & Chun, 2005), combined
with the fact that attention enhances memory encoding, further suggests that repetition
suppression may be a neural marker of memory formation. Together, these findings are
consistent with an account of how attention and memory interact that is centered on biased
competition during sensory processing.
Beyond the visual system, several regions of frontoparietal cortex are thought to exert
attentional control over the way stimuli are represented and routed in the brain (see Noudoost et
al., 2010). It follows that these regions may also influence the way in which memories are
encoded. In particular, these regions are connected into two networks — the dorsal attention
network involved in top-down, goal-directed control and the ventral attention network involved
in stimulus-driven reorienting — and these networks can have opposite effects on memory.
During encoding, greater activity in dorsal attention regions such as intraparietal sulcus (IPS) is
associated with better memory (Uncapher et al., 2011), whereas greater activity in ventral
attention regions such as temporoparietal junction (TPJ) is associated with worse memory
(Uncapher et al., 2011; Turk-Browne et al., 2013). These networks may impact memory by
modulating stimulus representations and biasing competition in visual cortex: functional
connectivity between visual regions and IPS prior to encoding predicts better memory, whereas
functional connectivity between visual regions and TPJ during encoding predicts worse memory
(Uncapher et al., 2011). Thus, the dorsal attention network may facilitate visual processing to
enhance the information available for memory encoding, whereas the ventral attention network
may redirect processing away from relevant stimuli, reducing the information available.
In addition to shifting functional connectivity between visual and frontoparietal regions,
attention can also switch connectivity within the visual system. Specifically, attention may alter
the flow of information along the sensory hierarchy based on task goals (Al-Aidroos et al., 2012;
Bosman et al., 2012). For example, low-level visual regions show increased functional
connectivity with the fusiform face area (FFA) when attending to faces and with the
parahippocampal place area (PPA) when attending to scenes (Al-Aidroos et al., 2012). Similar
modulation has been observed in the MTL, with ventral visual cortex switching functional
connectivity between parahippocampal cortex and perirhinal cortex during scene vs. face
attention, respectively (Córdova et al., 2016). It remains an open question how these connectivity
patterns influence hippocampal representation and episodic memory encoding.
Indeed, the effects of attention on the hippocampus may be qualitatively different than
effects of attention on cortex. Studies of both animal (Kentros et al., 2004; Muzzio et al., 2009;
Fenton et al., 2010) and human (Aly & Turk-Browne, 2016a; Aly & Turk-Browne, 2016b;
Córdova et al., 2019) hippocampus suggest that, rather than increasing the amplitude of neural
activity, attention increases the stability of neural activity patterns. Specifically, the hippocampus
shows sensitivity to attentional state such that, for example, attending to spatial information
evokes similar patterns of activity in the hippocampus across trials, whereas attending to item
information evokes a different, yet reliable pattern of activity in the hippocampus (Muzzio et al.,
2009; Aly & Turk-Browne, 2016a). Furthermore, the stabilization of hippocampal
representations by attention is related to episodic memory encoding (Figure 3). Greater pattern
similarity to an attentional state template is associated with better subsequent memory for the
corresponding task-relevant features of a stimulus (Aly & Turk-Browne, 2016b).
Where does the attentional modulation of hippocampus originate? In other words, is
representational stability in the hippocampus merely a different manifestation of the feedforward
influence of attention on the sensory hierarchy, or does it reflect a more direct routing of
attentional signals to the hippocampus through neuromodulation? For example, reward and
arousal — which both bias attention — may act on the hippocampal system directly via
dopaminergic (Shohamy & Adcock, 2010) and noradrenergic (Mather et al., 2016)
neurotransmitter systems, respectively. The acetylcholine system has also been implicated in
both attention and memory (Newman et al., 2012). Future studies examining the timing of
attentional signals in sensory cortex, attentional control networks, and the hippocampus, as well
as signals relating to neuromodulation, will shed light on the nature of attentional modulation of
the hippocampus.
B
same - different template
pattern similarity r()
A
Figure 3. Hippocampal representations are modulated by attention in a way that supports
episodic memory encoding (Aly & Turk-Browne, 2016b). (A) Analysis approach and task
design. In phase 1, participants were presented with rendered images of rooms in an art
museum and instructed to attend either to the layout of the room or the style of the art.
Voxel patterns of fMRI activity in the hippocampus from this phase were used as
attentional templates for room and art attentional states, respectively. In phase 2,
participants incidentally encoded similar but trial-unique images while performing one of
two related attention tasks. In art blocks, participants detected one-back repetitions of
artwork in the same style/by the same artist (despite being different pieces). In room blocks,
participants detected one-back repetitions of the layouts (despite surface differences). The
pattern of hippocampal activity for each encoding trial was correlated with the attentional
templates for the same vs. different attentional states. In phase 3, participants completed a
surprise memory test for the images from phase 2. (B) Subsequently remembered items
exhibited greater pattern similarity to the template that matched during their attentional
state incidental encoding than to the other template. In other words, memory was enhanced
when the hippocampus was in the correct attentional state during encoding. Adapted with
permission from Aly & Turk-Browne (2016b).
Attention during episodic retrieval
The influence of attention on episodic memory arises not only at the time of encoding,
determining which information gets stored, but also at the time of retrieval, determining which
information gets accessed and reported (see Tarder-Stoll et al., 2020). Indeed, attention can be
oriented internally to long-term episodic memories, just as it can be allocated to the contents of
short-term, working memories (Chun & Johnson, 2011). This analogy is further supported by
findings that episodic memory retrieval can be impaired by the same kinds of distraction as
working memory (Fernandes & Moscovitch, 2000; Wais & Gazzaley, 2011), presumably
because the distractors compete with the contents of memory for attentional resources (Wais et
al., 2010), and by findings that eye movements are spontaneously biased toward the encoded
locations of retrieved information (Johansson et al., 2012; Johansson & Johansson, 2014).
Dividing attention at retrieval also reduces the likelihood that an item will be retrieved again in a
later test (Dudukovic et al., 2009), suggesting that attention also plays a role in reinforcing and/or
re-encoding memories at retrieval.
These combined effects on both encoding and retrieval position attention as important for
disambiguating novel from familiar inputs. This role is echoed by studies of attentional
modulation during hippocampal retrieval. Computational models (e.g., Lisman & Grace, 2005)
and fMRI evidence (Kumaran & Maguire, 2006; Kumaran & Maguire, 2007; Chen et al., 2011;
Duncan et al., 2012) implicate the hippocampus in mismatch detection, when a retrieved memory
(e.g., an expectation of familiarity) conflicts with current sensory experience (i.e., unexpected
novelty). Mismatch signals are sensitive to attentional demands. For example, MTL activity is
enhanced when judging the novelty but not the recency of an item (Dudukovic & Wagner, 2007).
Novelty signals in the hippocampus are also sensitive to attentional state, as demonstrated by
dissociations across recognition memory tasks involving old, similar, and new items (Hashimoto
et al., 2012). When attending to perceptual attributes, the hippocampus responds equivalently to
similar and new items (reflecting the need to classify similar items as “new”), whereas when
attending to semantic attributes, the hippocampus responds equivalently to similar and old items
(reflecting the need to classify similar items as “old”). This is consistent with findings from
intracranial EEG, in which hippocampal activity distinguishes similar vs. old items only when
the task required such a discrimination (Lohnas et al., 2018).
In addition to the hippocampus, the parietal cortex has been consistently implicated in
the retrieval of memories (Wagner et al., 2005; Kuhl & Chun, 2014). Given that the parietal
cortex is strongly implicated in attention (Corbetta & Shulman, 2002; Xu, 2018), the role of the
parietal regions in episodic retrieval was thought to be reflective of its role in attentional
selection (Cabeza et al., 2008; Ciaramelli et al., 2008). Notably, however, discrete anatomical
subregions of parietal cortex seem to support attention vs. memory retrieval (Hutchinson et al.,
2009). Indeed, separate functional subregions of the parietal cortex track successful memory
recollection vs. attentional benefits, with some subregions only tracking memory performance,
and others tracking attentional components of memory retrieval (Hutchinson et al., 2014).
Despite debate about the precise anatomical substrates of attention vs. memory functions
in parietal cortex, there is evidence of a unique role for parietal cortex in attention-like memory
selection (Figure 4). Specifically, attentional effects on retrieval in frontal and parietal regions
can be dissociated from effects in the MTL (Kuhl et al., 2013). In a study designed to tease apart
the contributions of these regions to memory retrieval, participants first learned associations
between a word and either a face or a scene that appeared on either the left or right of the screen.
During a subsequent test phase, participants were cued with a word and asked to report either the
category or the location of the associated image. Multi-voxel pattern analysis (MVPA) was
applied during the test trials to decode evidence for face vs. scene categories. Such category
information could be decoded reliably from frontoparietal regions, but not medial temporal lobe
regions, on trials that required category retrieval. However, on location retrieval trials, in which
the category information was retrieved incidentally (as assessed in a later post-test), medial
temporal lobe regions but not frontoparietal regions exhibited reliable category evidence.
A
B
Figure 4. Frontoparietal regions support goal-directed long-term memory retrieval. (A)
During study, participants were presented with word-face or word-scene pairings. After
each round of study, they were shown a word and asked to recall one of two source features,
either whether the word was presented with a face or a scene, or whether the word’s
associated face/scene was presented on the left of the right of the screen. Subsequently, in
a surprise post-test they were presented again with words and were asked to recall both the
associated category and location, regardless of which was tested initially. (B) Category
evidence from the initial test, as a function of post-test performance. On trials in which the
category association was tested initially, greater category evidence in prefrontal cortex
(PFC) and lateral parietal cortex (LPC) predicted better subsequent memory. In the MTL,
category evidence on trials in which the location association was tested initially predicted
subsequent memory. Adapted with permission from Kuhl et al. (2013).
These findings suggest that parietal cortex may be particularly important for
implementing task goals and selecting relevant content from memory. In particular, dorsal and
lateral parietal regions — implicated in top-down attentional control (see Corbetta & Shulman,
2002) — are sensitive to retrieval goals, such that the contents of memory representations reflect
which feature was cued (Favila et al., 2018). As further support for the role of parietal cortex in
memory retrieval, visual information is represented more strongly in lateral parietal regions
during memory retrieval than during perception, whereas the reverse is true in occipitotemporal
cortex (Xiao et al., 2017; Favila et al., 2018).
Although interpreting the role of the parietal cortex in memory retrieval as an attentional
selection mechanism is appealing, it is important to note that the evidence above demonstrates
that parietal representations can be modulated by attentional state or goal-relevance. Whether the
parietal cortex plays a critical role in the selection of memories at retrieval remains an open
question. Specifically, patients with parietal lesions do not exhibit profound deficits in memory
retrieval (see Cabeza et al., 2008); rather, when reported, the deficits are subtle, such as
decreased vividness of memory (Berryhill et al., 2007) and a reduced sense of recollection (as
opposed to general familiarity; Davidson et al., 2008). Nonetheless, regardless of the precise
neural substrates, considering memory retrieval as a form of attentional selection remains a
useful framework (e.g., Anderson & Spellman, 1995; Levy & Anderson, 2002), not only for
understanding the effects of attention on memory retrieval, but also for understanding the general
properties of memory retrieval across memory systems such as working memory and episodic
memory.
Guidance of attention by episodic memory
The previous section reviewed how attention can guide memory retrieval, but retrieved
memories in turn play an important role in guiding attention (see Desimone, 1996; Hutchinson &
Turk-Browne, 2012). An early demonstration of such memory-guided attention compared how
attention is cued by past experience vs. current sensory information (Summerfield et al., 2006).
In this study, participants were pre-exposed to visual scenes and tasked with finding a hidden key
target. One day later, participants returned to perform a covert (without eye movements) visual
search task in which they again had to find keys. Some of the scenes were familiar from the prior
day (memory cue) and other scenes were novel but presented with a brief additional stimulus that
oriented participants to the location of the target (visual cue). Each condition was contrasted
against a baseline of scenes with equal viewing time but no cue (i.e., familiar scenes that did not
have a learned target and novel scenes with a neutral cue, respectively). As expected, visual
search was facilitated for the visual-cue condition relative to baseline. However, visual search
was also facilitated in the memory-cue condition, even though the target location was only
indicated by episodic memory (with no explicit visual cues). Consistent with the role of longterm memory in guiding attention, activity in the hippocampus selectively predicted the
performance benefit on the memory-cue trials.
Subsequent work using a similar paradigm established that episodic memory guides
attention by modulating perceptual processing (Stokes et al., 2012). Both EEG and fMRI
correlates of neural responses in visual cortex were modulated by the presence of a memory cue.
This can be interpreted as a memory-driven preparatory response in visual cortex that facilitated
processing, analogous to the biasing signals observed in standard goal-directed attention (see
Noudoost et al., 2010). One key difference from goal-directed attention, however, is that
memory-guided attention may reflect hippocampal control over visual cortex, rather than
frontoparietal control. Indeed, memory-guided attention can affect processing without an explicit
instruction or orientation towards the target (i.e., in the absence of a visual search task), and even
after just a single encoding opportunity (Becker & Rasmussen, 2008; Ciaramelli et al., 2009).
The proposed role of episodic memory in guiding attention is further supported by
evidence that memory — and the hippocampus — are important for determining where we move
our eyes (see Meister & Buffalo, 2016). This observation has been exploited to study memory in
infants (see Chapter 8.2). For example, in the visual paired comparison task, infants tend to look
more to novel than familiar stimuli all else being equal (see Rose et al., 2004). Adults and nonhuman primates also exhibit preferences for novel stimuli (e.g., Manns et al., 2000; Zola et al.,
2000). Preferential looking towards novel stimuli is adaptive, as it allows an observer to
prioritize extracting new information from the world rather than perseverating on information
that is already known.
Relatedly, when a familiar stimulus has changed in some way, it would be adaptive to
update this representation. Indeed, participants also preferentially look to aspects of remembered
stimuli that have changed. This was demonstrated by an early study linking the hippocampus to
eye movements (Ryan et al., 2000). In this study, participants first encoded a set of scenes and
then were presented with the scenes again after they had been altered by changing the presence
or relations of objects in each scene. Healthy individuals tended to look more at the manipulated
region of the image, whereas patients with hippocampal damage and associated amnesia did not.
In other words, overt attention was guided by discrepancies between perception and memory, a
process which depended on the hippocampus. These results dovetail with the previously
discussed findings that the hippocampus is sensitive to mismatches between perceived and
remembered information (Lisman & Grace, 2005) and critically highlight how such mismatch
representations may be consequential for guiding attention.
Similar guidance by relational memory has been observed in other paradigms. For
example, when faces are superimposed on scenes during encoding, healthy individuals look
longer during test at a face that matches the current scene than at other, equally familiar faces
that had been paired with other scenes. This behavior is correlated with activation of the
hippocampus in fMRI (Hannula & Ranganath, 2009) and is eliminated in patients with
hippocampal damage (Hannula et al., 2007). These effects occur rapidly upon encountering
familiar stimuli, suggesting that memory interacts with attentional processes triggered by the
sensory stimulus itself (Ryan et al., 2007). Interestingly, hippocampal guidance of overt attention
can be detected even in the absence of explicit memory for the familiar stimulus (Hannula &
Ranganath, 2009; Ryals et al., 2015).
Notably, many of the studies above are interpreted as an eye-movement measure of
memory, rather than an effect of memory on attention. However, in the real-world, we are rarely
confronted with the task of merely retrieving memories. Rather, we use memory to guide future
behavior, and these memory-related eye movements may play an important role in guiding our
attention. This form of attention is overt, as compared to the guidance of covert attention that
occurs while, for example, fixating during visual search. Nevertheless, such overt attention can
have dramatic effects on how we select and represent the sensory environment, including
because foveal (vs. peripheral) vision is much higher fidelity. Given this attentional guidance by
memory, it seems that memory actually guides memory — we use memory to orient our attention
in the world, which in turn influences what we subsequently encode into memory.
Attention and learning
The sections above considered interactions between attention and memory formed after
one or a small number of exposures, as is typical for working memory and episodic memory.
However, we regularly encounter the same items over time, in similar or different viewpoints,
contexts, and tasks. This repeated and varied exposure can enrich our memories for objects,
places, or people, resulting in abstracted representations stripped of idiosyncratic features, which
generalize better to future experiences and are more useful for prediction and facilitating
processing (see Chapters 2.2, 2.8, 6.6, and 7.1). Such memories must be held separate from
representations of individual experiences in episodic memory, which depend critically on
idiosyncratic features to enable the retrieval of specific moments in time and space.
Generalized memory representations must be learned over multiple exposures in which
individual (episodic) experiences are integrated rapidly or gradually over time (McClelland et al.,
1995; Schapiro et al., 2017). Here, we focus on two such forms of memory integration —
statistical learning and category learning — though other forms of learning including
reinforcement learning (Mackintosh, 1975; Niv et al., 2015; see Chapter 4.4) and motor skill
learning (Nissen & Bullemer, 1987; Wulf et al., 1998; see Chapters 2.4 and 11.4) also have deep
interactions with attention.
Attention and statistical learning
To study how people learn regularities across repeated experiences, a process known as
statistical learning, participants are repeatedly presented with configurations (e.g., Fiser & Aslin,
2001) or sequences (e.g., Turk-Browne et al., 2005) of stimuli with hidden structure. For
example, shapes might appear sequentially over time in repeated groupings of three, where the
first shape (A) is always followed by the second (B) and then the third (C). What appears before
A and after C is random and there are no other indications of where the groupings begin and end,
necessitating the extraction of transition probabilities to find the boundaries.
The emerging picture about the role of attention in statistical learning is complex. On one
hand, it is impossible to know in advance, or even after a few experiences, which items will be
part of regularities, and thus it is important to track multiple sets of statistics in the environment.
On the other hand, truly tracking all possible regularities would subject the mind to
combinatorial explosion. The balance of evidence suggests that attention does constrain
statistical learning, determining the stimuli over which regularities can be extracted (Jiménez &
Méndez, 1999; Jiang & Chun, 2001; Baker et al., 2004; Toro et al., 2005; Turk-Browne et al.,
2005; Emberson et al., 2011; Campbell et al., 2012; cf. Saffran et al., 1997; Musz et al., 2015).
Whether attention is strictly required for any learning to take place or serves a more modulatory
role, whether the influence of attention on learning reflects limited constraints on processing
resources (Desimone & Duncan, 1995) or whether attention is selectively allocated in order to
uncover relevant structure (Dayan et al., 2000), and how effects differ across tasks (Vickery et
al., 2018) and modalities (Frost et al., 2015) remain important questions.
Similar to the bidirectional interactions between episodic memory and attention, not only
does attention influence statistical learning, but regularities also capture and guide attention. For
example, in a visual search task, if the configuration of distractors is repeated and fixed with
respect to the target location, participants get faster at responding to the target, an effect known
as contextual cuing (Chun & Jiang, 1998). Visual search is also facilitated when target location
or identity is paired to distractors that have a consistent identity, even if their locations are
variable across trials (Chun & Jiang, 1999; Endo & Takeda, 2004). This and other work suggest
that multiple aspects of visual context can guide attention. Such effects can be viewed as
resulting from spatial or object-based statistical learning (Orbán et al., 2008; Vickery & Jiang,
2009) and/or from gradual encoding of relational memory (Ryan et al., 2000; Summerfield et al.,
2006). Moreover, attention is not only guided towards regions of space that contain targets, but
also away from regions containing distracting information (Wang & Theeuwes, 2018).
Furthermore, attention can be biased by regularities even in cases where the regularities
do not provide predictive information useful for a task. This has been demonstrated by
presenting multiple task-irrelevant streams of shapes at the same time and interrupting the
streams occasionally with a visual search trial (Zhao et al., 2013). When one of the streams
contains regularities, visual search is facilitated at that location, suggesting that the stream had
drawn attention. Critically, the location of regularities was randomized with respect to the search
targets, so this attentional bias occurred automatically.
This was replicated in additional experiments where multiple shape streams differing in
color or orientation were interleaved at a single, central location. Regularities in one of those
streams induced attentional capture on the interspersed visual search trials for the corresponding
dimension. For example, if the sequence of green shapes contained embedded structure, then
visual search was faster when the target was green and slower when a distractor was green,
compared to a different color like red associated with a random stream. Thus, despite
competition amongst multiple streams of information, attention can be spontaneously biased
towards the information that contains reliable structure. This has also been demonstrated in
infants, who preferentially attend to sources of information with moderately complex regularities
that need to be learned (Kidd et al., 2012; Kidd et al., 2014), perhaps a mechanism that allows
infants to efficiently acquire the structure of their environment (Saffran et al., 1996; Kirkham et
al, 2002; Aslin, 2017; Saffran & Kirkham, 2018).
The guidance of attention by regularities can flexibly influence not only the locus but also
the spatial scale of attention. Regularities at a global scale drive more global allocation of
attention, whereas regularities at a local scale drive attention to local details (Zhao & Luo, 2017).
In addition, statistical learning induces object-based attention effects, such that presenting targets
at either side of two paired objects produced faster detection than presenting targets at either side
of two shapes which were not paired together in learning (Zhao et al., 2014). Thus, regularities
can also guide attention within objects learned from repeated conjunctions of features and parts.
Why is attention automatically drawn towards regularities? One reason is that structure
often conveys goal-relevant information, such as in contextual cuing where the learned structure
reliably predicts the target location. Attentional biases to structure may also serve an adaptive
purpose in facilitating additional or new learning (Kosie & Baldwin, 2019). There is a chickenand-egg problem, however, with attention needed for learning but learning helping to guide
attention. A potential solution could be that simple regularities (e.g., stimulus repetition) might
be detected without attention, but once learned, draw attention. This may in turn permit more
sophisticated learning, a greater attentional bias, etc., until sufficient learning has occurred and
attention can be redirected elsewhere. An alternative solution could be that attention is deployed
in an exploratory manner in novel environments, and when it happens to fall on a location or
feature with structure, rapid learning captures attention and reduces further exploration.
Consistent with this, attention remains biased towards locations that contained structure even
after the regularities are replaced by randomness or another unknown set of regularities (Yu &
Zhao, 2015). Another interpretation of this emerging literature is that attention is repelled away
from randomness rather than towards structure. Indeed, initially encountering random
information in a configuration or sequence prior to regularities prevents statistical learning of
structure that emerges later (Jungé et al., 2007; Gebhart et al., 2009). Future work is needed to
further clarify the nature and purpose of interactions between attention and statistical learning,
perhaps by distinguishing between different conceptions of attention and by comparing different
measures of statistical learning.
Attention and category learning
The term statistical learning is often applied to the learning of regularities in how
multiple objects are organized over space and time. However, there are also regularities across
experiences with individual objects, resulting in abstract categories that structure our world into
types and concepts. As with statistical learning, attention plays an important role in category
learning. Attention can alter how stimuli are represented in both the mind and brain, leading to
different patterns of category classification in different contexts (e.g., Nosofsky, 1986; Johansen
& Palmeri, 2002; Mack et al., 2016). Task context can in turn affect attention and influence
categorization. For example, interleaved study of two categories biases attention to features that
differentiate between the two categories, whereas blocked study of a single category results in
attention to features shared within the category (Carvalho & Goldstone, 2017). Furthermore,
researchers have distinguished between perceptual attention, which is driven by the physical
features of a stimulus such as salience and is correlated with the speed of processing, and
decisional attention, which is driven by the relation of features to a category boundary and
classification. Perceptual attention may influence category decisions based on a single feature,
whereas decisional attention dominates when categorization requires the integration of features
across diagnostic dimensions (e.g., Maddox, 2002).
Consistent with these findings, models of category learning — in both behavior and
neural implementation — consider attention as an important intermediary for learning the
mapping between objects and categories (e.g., Kruschke, 1992; Minda & Smith, 2002; Love et
al., 2004). Accordingly, attention can be used to arbitrate between models of category learning,
for example, by measuring eye movements during a categorization task (Rehder & Hoffman,
2005). In this study, fixations shifted towards highly diagnostic features and away from the least
diagnostic features, reflecting learning of the relevant category features. However, in the last
block of learning, participants still reliably fixated the least diagnostic dimension, suggesting a
sub-optimal allocation of attention. This pattern of attention is consistent with exemplar rather
than prototype models of categorization: prototype models suggest that we represent a template
containing the most common, or ideal, features of a category, whereas exemplar models posit
that we hold each example of a category in memory, and thus would store even idiosyncratic or
uninformative features. Indeed, although allocating attention to non-diagnostic features may be
sub-optimal from a category learning perspective (Minda & Smith, 2002), it may prove adaptive
for memory more generally. That is, learning about the world is not only about learning
categories, but also about forming memories of the particular experiences we have, idiosyncratic
features and all.
Conclusions and Open Questions
Throughout this chapter, we have highlighted interactions between multiple forms of
attention and memory. Across working memory, episodic memory, and statistical learning, the
same pattern of results emerges: attention is important for determining both whether a memory is
formed in the first place and, if so, what content it contains; and those memory representations
are in turn important for guiding attention.
In real life, attention and memory do not operate in isolation. Rather, our mind
seamlessly switches attention between information from the current sensory environment, the
environment from moments ago, and environments in the more distant past. As we move our
eyes across a scene, we perceptually register the part being sampled, we hold in working
memory the parts that are out of view, and these representations in turn trigger the retrieval of
episodic memories, regularities, and categories. The amalgam of this new experience — mixing
present with past — itself gets encoded into working memory and eventually long-term memory,
and gets abstracted over during statistical learning. This intricate and recursive web of perceptual
and mnemonic experiences highlights the complexity of interactions between cognitive systems:
where attention is allocated will influence what kind of memory is formed, and what kind of
memory is retrieved will influence the features to which attention is allocated. And because
attention has limited capacity over features, locations, and time (e.g., Shapiro et al., 1997; Lavie
et al., 2004; Franconeri et al., 2007), these interactions will not always be beneficial. For
example, attention to regularities may compete with attention to episodic details (Sherman &
Turk-Browne, 2020).
What, then, determines which memories guide attention? Not only might working
memory and long-term memory compete, but so too might different forms of long-term memory.
For example, attention can be guided not only by memory for contextual information stored in
the hippocampus, but also by memory for the mapping between the stimulus location and the
required response stored in the striatum (Goldfarb et al., 2016). That said, many of the
documented cases of attentional guidance from long-term memory are related to the
hippocampus. Not only is episodic memory-guided attention dependent upon the hippocampus
(e.g., Ryan et al., 2000), but so are contextual cuing (e.g., Chun & Phelps, 1999), statistical
learning (e.g., Schapiro et al., 2014), and category learning (e.g., Foerde et al., 2013). Thus,
competition within the hippocampus may serve to arbitrate between different kinds of memory
and ultimately play a critical role in determining the allocation of attention. Further work on how
competition between memory systems influences attention is needed.
The shared reliance of working memory and long-term memory on attention highlights
the importance of their relationship. What role does attention play in transforming information
held in working memory into long-term memory representations? How does information
retrieved from long-term memory get transferred into working memory for online use? Can
effects of long-term memory on attention be understood as merely effects of working memory on
attention? In this vein, it is also important to emphasize the ubiquity of attention in a variety of
cognitive processes. Considering how these other modulated factors, such as context, value, and
emotion, relate to attentional processing may help to clarify observed bidirectional interactions
between attention and memory.
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