Specificity and persistence of statistical learning in distractor suppression

Statistically learned associations among objects bias attention

A growing body of research suggests that semantic relationships among objects can influence the control of attention. There is also some evidence that learned associations among objects can bias attention. However, it is unclear whether these findings are due to statistical learning or existing semantic relationships. In the present study, we examined whether statistically learned associations among objects can bias attention in the absence of existing semantic relationships. Participants searched for one of four targets among pairs of novel shapes and identified whether the target was present or absent from the display. In an initial training phase, each target was paired with an associated distractor in a fixed spatial configuration. In a subsequent test phase, each target could be paired with the previously associated distractor or a different distractor. In our first experiment, the previously associated distractor was always presented in the same pair as the target. Participants were faster to respond when this distractor was present on target-present trials. In our second experiment, the previously associated distractor was presented in a different pair than the target in the test phase. In this case, participants were slower to respond when this distractor was present on both target-present and target-absent trials. Together, these findings provide clear evidence that statistically learned associations among objects can bias attention, analogous to the effects of semantic relationships on attention.

Suppression of a salient distractor protects the processing of target features

We are often bombarded with salient stimuli that capture our attention and distract us from our current goals. Decades of research have shown the robust detrimental impacts of salient distractors on search performance and, of late, in leading to altered feature perception. These feature errors can be quite extreme, and thus, undesirable. In search tasks, salient distractors can be suppressed if they appear more frequently in one location, and this learned spatial suppression can lead to reductions in the cost of distraction as measured by reaction time slowing. Can learned spatial suppression also protect against visual feature errors? To investigate this question, participants were cued to report one of four briefly presented colored squares on a color wheel. On two-thirds of trials, a salient distractor appeared around one of the nontarget squares, appearing more frequently in one location over the course of the experiment. Participants' responses were fit to a model estimating performance parameters and compared across conditions. Our results showed that general performance (guessing and precision) improved when the salient distractor appeared in a likely location relative to elsewhere. Critically, feature swap errors (probability of misreporting the color at the salient distractor's location) were also significantly reduced when the distractor appeared in a likely location, suggesting that learned spatial suppression of a salient distractor helps protect the processing of target features. This study provides evidence that, in addition to helping us avoid salient distractors, suppression likely plays a larger role in helping to prevent distracting information from being encoded.

The dynamics of statistical learning in visual search and its interaction with salience processing: An EEG study

Visual attention can be guided by statistical regularities in the environment, that people implicitly learn from past experiences (statistical learning, SL). Moreover, a perceptually salient element can automatically capture attention, gaining processing priority through a bottom-up attentional control mechanism. The aim of our study was to investigate the dynamics of SL and if it shapes attentional target selection additively with salience processing, or whether these mechanisms interact, e.g. one gates the other. In a visual search task, we therefore manipulated target frequency (high vs. low) across locations while, in some trials, the target was salient in terms of colour. Additionally, halfway through the experiment, the high-frequency location changed to the opposite hemifield. EEG activity was simultaneously recorded, with a specific interest in two markers related to target selection and post-selection processing, respectively: N2pc and SPCN. Our results revealed that both SL and saliency significantly enhanced behavioural performance, but also interacted with each other, with an attenuated saliency effect at the high-frequency target location, and a smaller SL effect for salient targets. Concerning processing dynamics, the benefit of salience processing was more evident during the early stage of target selection and processing, as indexed by a larger N2pc and early-SPCN, whereas SL modulated the underlying neural activity particularly later on, as revealed by larger late-SPCN. Furthermore, we showed that SL was rapidly acquired and adjusted when the spatial imbalance changed. Overall, our findings suggest that SL is flexible to changes and, combined with salience processing, jointly contributes to establishing attentional priority.

Investigating the role of spatial filtering on distractor suppression

In recent years, evidence has accumulated towards a distractor suppression mechanism that enables efficient selection of targets in a visual search task. According to these findings, the search for a target is faster in the presence of a salient distractor in a display among homogenous distractors as opposed to its absence. Studies have also shown that distractor suppression not only operates on the feature level but can also be spatially guided. The motivation of the current study was to examine if spatially guided distractor suppression can be goal-driven. We tested this across four experiments. In Experiment 1A, the task was to search for a shape target (e.g., a circle) and discriminate the orientation of the line within it. In some trials, a salient color distractor was presented in the display while participants were told that it appeared in one of the two locations on the horizontal axis (or the vertical axis, counterbalanced across participants). We expected enhanced distractor suppression when the salient distractor appeared within this "spatial filter" but did not find it since the target was also presented at the filtered locations. Experiment 1B replicated Experiment 1A, except that the target was always presented outside the filter; filtering enhanced search performance. In Experiment 2 even when the filter contained the salient distractor in only 65% of the filtered trials, filtering benefited search performance. In Experiment 3, the filter changed on every trial and did not benefit suppression.

The Electrophysiological Markers of Statistically Learned Attentional Enhancement: Evidence for a Saliency-based Mechanism

It is well established that attention can be sharpened through the process of statistical learning (e.g., visual search becomes faster when targets appear at high-relative-to-low probability locations). Although this process of statistically learned attentional enhancement differs behaviorally from the well-studied top-down and bottom-up forms of attention, relatively little work has been done to characterize the electrophysiological correlates of statistically learned attentional enhancement. It thus remains unclear whether statistically learned enhancement recruits any of the same cognitive mechanisms as top-down or bottom-up attention. In the current study, EEG data were collected while participants searched for an ambiguous unique shape in a visual array (the additional singleton task). Unbeknownst to the participants, targets appeared more frequently in one location in space (probability cuing). Encephalographic data were then analyzed in two phases: an anticipatory phase and a reactive phase. In the anticipatory phase preceding search stimuli onset, alpha lateralization as well as the Anterior Directing Attention Negativity and Late Directing Attention Positivity components-signs of preparatory attention known to characterize top-down enhancement-were tested. In the reactive phase, the N2pc component-a well-studied marker of target processing-was examined following stimuli onset. Our results showed that statistically learned attentional enhancement is not characterized by any of the well-known anticipatory markers of top-down attention; yet targets at high probability locations did reliably evoke larger N2pc amplitudes, a finding that is associated with bottom-up attention and saliency. Overall, our findings are consistent with the notion that statistically learned attentional enhancement increases the perceptual salience of items appearing at high-probability locations relative to low-probability locations.

Learning to suppress a location is configuration-dependent

Where and what we attend is very much determined by what we have encountered in the past. Recent studies have shown that people learn to extract statistical regularities in the environment resulting in attentional suppression of locations that were likely to contain a distractor, effectively reducing the amount of attentional capture. Here, we asked whether this suppression effect due to statistical learning is dependent on the specific configuration within which it was learned. The current study employed the additional singleton paradigm using search arrays that had a configuration consisting of set sizes of either four or 10 items. Each configuration contained its own high probability distractor location. If learning would generalize across set size configurations, both high probability locations would be suppressed equally, regardless of set size. However, if learning to suppress is dependent on the configuration within which it was learned, one would expect only suppression of the high probability location that matched the configuration within which it was learned. The results show the latter, suggesting that implicitly learned suppression is configuration-dependent. Thus, we conclude that the high probability location is learned within the configuration context within which it is presented.

Selection history contributes to suboptimal attention strategies

Attentional control balances the competing drives of performance maximization and effort minimization. One way the attention system minimizes effort is through a bias to persist in the use of attentional control strategies that have been useful in the past. In the present study, we asked whether such selection history can result in the persistence of an attentional control strategy that is counterproductive, effectively competing with a more optimal strategy. Participants first completed a training in which one color target was encountered more frequently than another, and then completed a test phase in which they could search for one of two targets on any given trial, one of which would be more optimal to search for given the distribution of color stimuli. An attentional bias for the more frequent target color was observed in the training phase and the choice of which target to report was robustly optimal in the test phase, reflecting performance maximization. Importantly, participants also exhibited a tendency to report the target rendered in the previously more frequent target color in the test phase, even when the distribution of non-target colors made it suboptimal to do so. Our findings shed light on the fundamental question of why attentional control is sometimes suboptimal, demonstrating a role for selection history in the perseveration of previously employed attentional strategies even when such strategies produce suboptimal performance.

Expected distractor context biases the attentional template for target shapes

Visual search is supported by an internal representation of the target, the attentional template. However, which features are diagnostic of target presence critically depends on the distractors. Accordingly, previous research showed that consistent distractor context shapes the attentional template for simple targets, with the template emphasizing diagnostic dimensions (e.g., color or orientation) in blocks of trials. Here, we investigated how distractor expectations bias attentional templates for complex shapes, and tested whether such biases reflect intertrial priming or can be instantiated flexibly. Participants searched for novel shapes (cued by name) in two probabilistic distractor contexts: Either the target's orientation or rectilinearity was unique (80% validity). Across four experiments, performance was better when the distractor context was expected, indicating that target features in the expected diagnostic dimension were emphasized. Attentional templates were biased by distractor expectations when distractor context was blocked, also for participants reporting no awareness of the manipulation. Interestingly, attentional templates were also biased when distractor context was cued on a trial-by-trial basis, but only when the two contexts were consistently presented at distinct spatial locations. These results show that attentional templates can flexibly and adaptively incorporate expectations about target-distractor relations when looking for the same object in different contexts. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Neural correlates of value-driven spatial orienting

Reward learning has been shown to habitually guide overt spatial attention to specific regions of a scene. However, the neural mechanisms that support this bias are unknown. In the present study, participants learned to orient themselves to a particular quadrant of a scene (a high-value quadrant) to maximize monetary gains. This learning was scene-specific, with the high-value quadrant varying across different scenes. During a subsequent test phase, participants were faster at identifying a target if it appeared in the high-value quadrant (valid), and initial saccades were more likely to be made to the high-value quadrant. fMRI analyses during the test phase revealed learning-dependent priority signals in the caudate tail, superior colliculus, frontal eye field, anterior cingulate cortex, and insula, paralleling findings concerning feature-based, value-driven attention. In addition, ventral regions typically associated with scene selection and spatial information processing, including the hippocampus, parahippocampal gyrus, and temporo-occipital cortex, were also implicated. Taken together, our findings offer new insights into the neural architecture subserving value-driven attention, both extending our understanding of nodes in the attention network previously implicated in feature-based, value-driven attention and identifying a ventral network of brain regions implicated in reward's influence on scene-dependent spatial orienting.

Pinging the brain to reveal the hidden attentional priority map using encephalography

Attention has been usefully thought of as organized in priority maps - putative maps of space where attentional priority is weighted across spatial regions in a winner-take-all competition for attentional deployment. Recent work has highlighted the influence of past experiences on the weighting of spatial priority - called selection history. Aside from being distinct from more well-studied, top-down forms of attentional enhancement, little is known about the neural substrates of history-mediated attentional priority. Using a task known to induce statistical learning of target distributions, in an EEG study we demonstrate that this otherwise invisible, latent attentional priority map can be visualized during the intertrial period using a 'pinging' technique in conjunction with multivariate pattern analyses. Our findings not only offer a method of visualizing the history-mediated attentional priority map, but also shed light on the underlying mechanisms allowing our past experiences to influence future behavior.

Statistical learning of distractor locations is dependent on task context

Through statistical learning, humans can learn to suppress visual areas that often contain distractors. Recent findings suggest that this form of learned suppression is insensitive to context, putting into question its real-life relevance. The current study presents a different picture: we show context-dependent learning of distractor-based regularities. Unlike previous studies which typically used background cues to differentiate contexts, the current study manipulated task context. Specifically, the task alternated from block to block between a compound search and a detection task. In both tasks, participants searched for a unique shape, while ignoring a uniquely colored distractor item. Crucially, a different high-probability distractor location was assigned to each task context in the training blocks, and all distractor locations were made equiprobable in the testing blocks. In a control experiment, participants only performed a compound search task such that the contexts were made indistinguishable, but the high-probability locations changed in exactly the same way as in the main experiment. We analyzed response times for different distractor locations and show that participants can learn to suppress a location in a context-dependent way, but suppression from previous task contexts lingers unless a new high-probability location is introduced.

A neurophysiological perspective on the integration between incidental learning and cognitive control

Adaptive behaviour requires interaction between neurocognitive systems. Yet, the possibility of concurrent cognitive control and incidental sequence learning remains contentious. We designed an experimental procedure of cognitive conflict monitoring that follows a pre-defined sequence unknown to participants, in which either statistical or rule-based regularities were manipulated. We show that participants learnt the statistical differences in the sequence when stimulus conflict was high. Neurophysiological (EEG) analyses confirmed but also specified the behavioural results: the nature of conflict, the type of sequence learning, and the stage of information processing jointly determine whether cognitive conflict and sequence learning support or compete with each other. Especially statistical learning has the potential to modulate conflict monitoring. Cognitive conflict and incidental sequence learning can engage in cooperative fashion when behavioural adaptation is challenging. Three replication and follow-up experiments provide insights into the generalizability of these results and suggest that the interaction of learning and cognitive control is dependent on the multifactorial aspects of adapting to a dynamic environment. The study indicates that connecting the fields of cognitive control and incidental learning is advantageous to achieve a synergistic view of adaptive behaviour.

Learned feature regularities enable suppression of spatially overlapping stimuli

Contemporary theories of attentional control state that information can be prioritized based on selection history. Even though theories agree that selection history can impact representations of spatial location, which in turn helps guide attention, there remains disagreement on whether nonspatial features (e.g., color) are modulated in a similar way. While previous work has demonstrated color suppression using visual search tasks, it is possible that the location corresponding to the distractor was suppressed, consistent with a spatial mechanism of suppression. Here, we sought to rule out this possibility by testing whether similar suppression of a learned distractor color can occur for spatially overlapping visual stimuli. On a given trial, two spatially superimposed stimuli (line arrays) were tilted either left or right of vertical and presented in one of four distinct colors. Subjects performed a speeded report of the orientation of the “target” array with the most lines. Critically, the distractor array was regularly one color, and this high-probability color was never the color of the target array, which encouraged learned suppression. In two experiments, responses to the target array were fastest when the distractor array was in the high-probability color, suggesting participants suppressed the distractor color. Additionally, when regularities were removed, the high-probability distractor color continued to benefit speeded target identification for individual subjects (E1) but slowed target identification (E2) when presented in the target array. Together, these results indicate that learned suppression of feature-based regularities modulates target detection performance independent of spatial location and persists over time.

This is a test: Oculomotor capture when the experiment keeps score

Physically salient stimuli are difficult to ignore, frequently eliciting fixations even when they are known to be task-irrelevant. A recent study demonstrated that distractor fixation-contingent auditory feedback was highly effective in reducing the frequency of fixations on such stimuli. The present study explores more specifically what it is about feedback that makes it effective in curbing oculomotor behavior. In one experiment, we removed the immediacy of the feedback by informing participants after each trial via textual feedback if they had fixated the distractor. A comparable reduction in the frequency of oculomotor capture was observed. In a second experiment, we only provided summary feedback concerning the frequency of oculomotor capture after each block of trials. Not only were the benefits of feedback again robustly comparable, but a benefit was observed even in the first block before any feedback had actually been presented. Simply knowing that the frequency of distractor fixations was being monitored was sufficient to substantially reduce the frequency of oculomotor capture. Interestingly, trial-level feedback predominantly reduced the frequency of capture by slowing oculomotor responses, reflecting a speed-accuracy tradeoff, whereas block-wise feedback resulted in a reduction in the frequency of capture with saccadic reaction time equated, reflecting a bona fide improvement in task performance. Our findings have implications for our understanding of the role of motivation, strategy, and selection history in oculomotor control.

Systemic effects of selection history on learned ignoring

Despite our best intentions, physically salient but entirely task-irrelevant stimuli can sometimes capture our attention. With learning, it is possible to more efficiently ignore such stimuli, although specifically how the visual system accomplishes this remains to be clarified. Using a sample of young-adult participants, we examined the time course of eye movements to targets and distractors. We replicate a reduced frequency of eye movements to the distractor when appearing in a location at which distractors are frequently encountered. This reduction was observed even for the earliest saccades, when selection tends to be most stimulus-driven. When the distractor appeared at the high-probability location, saccadic reaction time was slowed specifically for distractor-going saccades, suggesting a slowing of priority accumulation at this location. In the event that the distractor was fixated, disengagement from the distractor was also faster when it appeared in the high-probability location. Both proactive and reactive mechanisms of distractor suppression work together to minimize attentional capture by frequently encountered distractors.

Habituation to abrupt-onset distractors with different spatial occurrence probability

Previous studies have shown that abrupt onsets randomly appearing at different locations can be ignored with practice, a result that was interpreted as an instance of habituation. Here we addressed whether habituation of capture can be spatially selective and determined by the rate of onset occurrence at different locations, and whether habituation is achieved via spatial suppression applied at the distractor location. In agreement with the habituation hypothesis, we found that capture attenuation was larger where the onset distractor occurred more frequently, similarly to what has been documented for feature-singleton distractors (the “distractor-location effect”), and that onset interference decreased across trials at both the high- and low-probability distractor locations. By contrast, evidence was inconclusive as to whether distractor filtering was also accompanied by a larger impairment in target processing when it appeared at the more likely distractor location (the “target-location effect”), as instead previously reported for feature-singleton distractors. Finally, here we discuss how and to what extent distractor rejection based on statistical learning and habituation of capture are different, and conclude that the two notions are intimately related, as the Sokolov model of habituation operates by comparing the upcoming sensory input with expectation based on the statistics of previous stimulation.

What to expect where and when: how statistical learning drives visual selection

While the visual environment contains massive amounts of information, we should not and cannot pay attention to all events. Instead, we need to direct attention to those events that have proven to be important in the past and suppress those that were distracting and irrelevant. Experiences molded through a learning process enable us to extract and adapt to the statistical regularities in the world. While previous studies have shown that visual statistical learning (VSL) is critical for representing higher order units of perception, here we review the role of VSL in attentional selection. Evidence suggests that through VSL, attentional priority settings are optimally adjusted to regularities in the environment, without intention and without conscious awareness.

Independence of implicitly guided attention from goal-driven oculomotor control

Location probability learning-the acquisition of an attentional bias toward locations that frequently contained a search target-shows many characteristics of a search habit. To what degree does it depend on oculomotor control, as might be expected if habit-like attention is grounded in eye movements? Here, we examined the impact of a spatially incompatible oculomotor signal on location probability learning (LPL). On each trial of a visual search task, participants first saccaded toward a unique C-shape, whose orientation determined whether participants should continue searching for a T target among L distractors. The C-shape often appeared in one, "C-rich" quadrant that differed from where the T was frequently located. Experiment 1 showed that participants acquired LPL toward the high-probability, "T-rich" quadrant, an effect that persisted in an unbiased testing phase. Participants were also faster finding the target in the vicinity of the C-shape, but this effect did not persist after the C-shape was removed. Experiment 2 found that the C-shape affected search only when it was task-relevant. Experiment 3 replicated and extended the findings of Experiment 1 using eye tracking. Thus, location probability learning is robust in the face of a spatially incompatible saccade, demonstrating partial independence between experience-guided attention and goal-driven oculomotor control. The findings are in line with the modular view of attention, which conceptualizes the search habit as a high-level process abstracted from eye movements.

Surprisingly inflexible: Statistically learned suppression of distractors generalizes across contexts

The present study investigates the flexibility of statistically learned distractor suppression between different contexts. Participants performed the additional singleton task searching for a unique shape, while ignoring a uniquely colored distractor. Crucially, we created two contexts within the experiments, and each context was assigned its own high-probability distractor location, so that the location where the distractor was most likely to appear depended on the context. Experiment 1 signified context through the color of the background. In Experiment 2, we aimed to more strongly differentiate between the contexts using an auditory or visual cue to indicate the upcoming context. In Experiment 3, context determined the appropriate response ensuring that participants engaged the context in order to be able to perform the task. Across all experiments, participants learned to suppress both high-probability locations, even if they were not aware of these spatial regularities. However, these suppression effects occurred independent of context, as the pattern of suppression reflected a de-prioritization of both high-probability locations which did not change with the context. We employed Bayesian analyses to statistically quantify the absence of context-dependent suppression effects. We conclude that statistically learned distractor suppression is robust and generalizes across contexts.

Is Statistical Learning of a Salient Distractor's Color Implicit, Inflexible and Distinct From Inter-Trial Priming?

Being able to overcome distraction by salient distractors is critical in order to allocate our attention efficiently. Previous research showed that observers can learn to ignore salient distractors endowed with some regularity, such as a high-probability location or feature - a phenomenon known as distractor statistical learning. Unlike goal-directed attentional guidance, the bias induced by statistical learning is thought to be implicit, long-lasting and inflexible. We tested these claims with regard to statistical learning of distractor color in a high-power (N = 160) pre-registered experiment. Participants searched for a known-shape singleton target and a color singleton distractor, when present, appeared most often in one color during the learning phase, but equally often in all possible colors during the extinction phase. We used a sensitive measure of participants' awareness of the probability manipulation. The awareness test was administered after the extinction phase for one group, and after the leaning phase for another group - which was informed that the probability imbalance would be discontinued in the upcoming extinction phase. Participants learned to suppress the high-probability distractor color very fast, an effect partly due to intertrial priming. Crucially, there was only little evidence that the bias survived during extinction. Awareness of the manipulation was associated with reduced color suppression, suggesting that the bias was implicit. Finally, results showed that the awareness test was more sensitive when administered early vs. late. We conclude that learnt color suppression is an implicit bias that emerges and decays rapidly, and discuss the methodological implications of our findings.

Oculomotor feedback rapidly reduces overt attentional capture

People often have limited awareness of the extent to which their attention is captured by salient-but-irrelevant stimuli. In the present study, we examined how providing feedback concerning the frequency of oculomotor capture by such stimuli modulates the control of attention. Our results show that the provision of oculomotor feedback produces a rapid and dramatic decrease in the frequency of distractor fixations. Further probing of this reduction in oculomotor capture by time to fixate the first stimulus revealed further insights into the nature of this experience-dependent effect. A higher frequency of relatively slow fixation latencies was observed in the feedback group, with such responses being generally less prone to capture, reflecting a speed-accuracy tradeoff. Fixations with slower latencies were also associated with a reduced frequency of oculomotor capture in the feedback group, whereas the fastest responses were almost exclusively stimulus-driven across participants and unaffected by feedback. These effects of feedback persisted when feedback was removed and they generalized to novel stimuli. Our findings suggest that, without any instruction concerning how to use the feedback, the oculomotor system defaults to delaying saccadic responses to allow more time for goal-directed influences on selection to come online, reflecting a history-dependent shift in oculomotor processing.

Spatial suppression due to statistical regularities in a visual detection task

Increasing evidence demonstrates that observers can learn the likely location of salient singleton distractors during visual search. To date, the reduced attentional capture at high-probability distractor locations has typically been examined using so called compound search, in which by design a target is always present. Here, we explored whether statistical distractor learning can also be observed in a visual detection task, in which participants respond target present if the singleton target is present and respond target absent when the singleton target is absent. If so, this allows us to examine suppression of the location that is likely to contain a distractor both in the presence, but critically also in the absence, of a priority signal generated by the target singleton. In an online variant of the additional singleton paradigm, observers had to indicate whether a unique shape was present or absent, while ignoring a colored singleton, which appeared with a higher probability in one specific location. We show that attentional capture was reduced, but not absent, at high-probability distractor locations, irrespective of whether the display contained a target or not. By contrast, target processing at the high-probability distractor location was selectively impaired on distractor-present displays. Moreover, all suppressive effects were characterized by a gradient such that suppression scaled with the distance to the high-probability distractor location. We conclude that statistical distractor learning can be examined in visual detection tasks, and discuss the implications for attentional suppression due to statistical learning.

Attention and distraction in the predictive brain

Whether it is possible to ignore a physically salient distractor has been a topic of active debate over the past 25 years, with empirical evidence for and against each of the theoretical stances. We put forward that predictive processing may provide a unified theoretical perspective that can account reasonably well for the empirical literature on attentional capture. In this perspective, capture is a logical consequence of the overall imperative of the brain to predict what sensory signals provide precise information to achieve goal-directed behaviour.

The past, present, and future of selection history

The last ten years of attention research have witnessed a revolution, replacing a theoretical dichotomy (top-down vs. bottom-up control) with a trichotomy (biased by current goals, physical salience, and selection history). This third new mechanism of attentional control, selection history, is multifaceted. Some aspects of selection history must be learned over time whereas others reflect much more transient influences. A variety of different learning experiences can shape the attention system, including reward, aversive outcomes, past experience searching for a target, target‒non-target relations, and more. In this review, we provide an overview of the historical forces that led to the proposal of selection history as a distinct mechanism of attentional control. We then propose a formal definition of selection history, with concrete criteria, and identify different components of experience-driven attention that fit within this definition. The bulk of the review is devoted to exploring how these different components relate to one another. We conclude by proposing an integrative account of selection history centered on underlying themes that emerge from our review.

Impaired selection of a previously ignored singleton: Evidence for salience map plastic changes

Spatial suppression of a salient colour distractor is achievable via statistical learning. Distractor suppression attenuates unwanted capture, but at the same time target selection at the most likely distractor location is impaired. This result corroborates the idea that the distractor salience is attenuated via inhibitory signals applied to the corresponding location in the priority map. What is less clear, however, is whether lingering impairment in target selection when the distractor is removed are due to the proactive strategic maintenance of the suppressive signal at the previous most likely distractor location or result from the fact that suppression has induced plastic changes in the priority map, probably changing input weights. Here, we provide evidence that supports the latter possibility, as we found that impairment in target selection persisted even when the singleton distractor in the training phase became the target of search in a subsequent test phase. This manipulation rules out the possibility that the observed impairments at the previous most likely distractor location were caused by a signal suppression maintained at this location. Rather, the results reveal that the inhibitory signals cause long-lasting changes in the priority map, which affect future computation of the target salience at the same location, and therefore the efficiency of attentional selection.

Attention and distraction in the predictive brain

Whether it is possible to ignore a physically salient distractor has been a topic of active debate over the past 25 years, with empirical evidence for and against each of the theoretical stances. We put forward that predictive processing may provide a unified theoretical perspective that can account reasonably well for the empirical literature on attentional capture. In this perspective, capture is a logical consequence of the overall imperative of the brain to predict what sensory signals provide precise information to achieve goal-directed behaviour.

Statistical learning of target selection and distractor suppression shape attentional priority according to different timeframes

Recent findings suggest that attentional and oculomotor control is heavily affected by past experience, giving rise to selection and suppression history effects, so that target selection is facilitated if they appear at frequently attended locations, and distractor filtering is facilitated at frequently ignored locations. While selection history effects once instantiated seem to be long-lasting, whether suppression history is similarly durable is still debated. We assessed the permanence of these effects in a unique experimental setting investigating eye-movements, where the locations associated with statistical unbalances were exclusively linked with either target selection or distractor suppression. Experiment 1 and 2 explored the survival of suppression history in the long and in the short term, respectively, revealing that its lingering traces are relatively short lived. Experiment 3 showed that in the very same experimental context, selection history effects were long lasting. These results seem to suggest that different mechanisms support the learning-induced plasticity triggered by selection and suppression history. Specifically, while selection history may depend on lasting changes within stored representations of the visual space, suppression history effects hinge instead on a functional plasticity which is transient in nature, and involves spatial representations which are constantly updated and adaptively sustain ongoing oculomotor control.

Gotcha: Working memory prioritization from automatic attentional biases

Attention is an important resource for prioritizing information in working memory (WM), and it can be deployed both strategically and automatically. Most research investigating the relationship between WM and attention has focused on strategic efforts to deploy attentional resources toward remembering relevant information. However, such voluntary attentional control represents a mere subset of the attentional processes that select information to be encoded and maintained in WM (Theeuwes, Journal of Cognition, 1[1]: 29, 1-15, 2018). Here, we discuss three ways in which information becomes prioritized automatically in WM-physical salience, statistical learning, and reward learning. This review integrates findings from perception and working memory studies to propose a more sophisticated understanding of the relationship between attention and working memory.

Punishment-modulated attentional capture is context specific

Attention prioritizes stimuli previously associated with punishment. Despite the importance of this process for survival and adaptation, the potential generalization of punishment-related attentional biases has been largely ignored in the literature. This study aimed to determine whether stimulus-punishment associations learned in a specific context bias attention in another context (in which the stimulus was never paired with punishment). We examined this issue using an antisaccade task in which participants had to shift their gaze in the opposite direction of a colored square during stimulus-outcome learning. Two contexts and three colors were employed. One color was associated with punishment (i.e., electrical shock) in one context and never paired with punishment in the other context. For a second color, the punishment-context relationship was reversed. A third color never paired with shock in either context (neutral) was included in Experiment 1 but absent in Experiment 2. Participants then performed search for a shape-defined target in an extinction phase (in which no shock was delivered) in which attentional bias for the colors was assessed. Context was manipulated via the background image upon which the stimuli were presented. In each of the two experiments, a bias to selectively orient toward the color that had been associated with punishment in the current context was observed, suggesting that punishment-modulated attentional priority is context specific.

Distractor filtering is affected by local and global distractor probability, emerges very rapidly but is resistant to extinction

Effects of statistical learning (SL) of distractor location have been shown to persist when the probabilities of distractor occurrence are equalized across different locations in a so-called extinction phase. Here, we asked whether lingering effects of SL are still observed when a true extinction phase, during which the distractor is completely omitted, is implemented. The results showed that, once established, the effects of SL of distractor location do survive the true extinction phase, indicating that the pattern of suppression in the saliency map is encoded in a form of long-lasting memory. Quite unexpectedly, we also found that the amount of filtering implemented at a given location is not only dictated by the specific rate of distractor occurrence at that location, as previously found, but also by the global distractor probability. We therefore suggest that the visual attention system could be more or less (implicitly) prone to suppression as a function of how often the distractor is encountered overall, and that this suppressive bias affects the degree of suppression at the specific distractor-probability location. Finally, our results showed that the effects of SL of distractor location can appear much more rapidly than has been previously documented, requiring a few trials to become manifest. Hence, SL of distractor location appears to have an asymmetrical rate of learning during acquisition and extinction, while the amount of suppression exerted at a specific distractor location is modulated by distractor contextual probabilistic information.

Context isn't everything: Search performance is influenced by the nature of the task but not the background

AbstractIt has been demonstrated in the literature that cues in the environment that are predictive of how a task ought to be performed can influence performance. In an extension of this general notion, Cosman and Vecera (Journal of Experimental Psychology: Human Perception and Performance, 39(3), 836-848, 2013) reported that simply performing singleton and feature search tasks when irrelevant scenes were displayed in the background automatically modulated the search strategies adopted by participants when these scenes were reinstated at a later time. While intriguing, this result was also somewhat surprising given that an adaptive system (like the human brain) should disregard irrelevant information so task competencies generalize across environments. To investigate this issue further, we replicated the experimental procedures of Cosman and Vecera, while varying whether the test phase was either a singleton search (Experiments 1 and 3) or a feature search (Experiment 2) task. While it was observed that the nature of the search task varied whether a color singleton distractor influenced performance, there was no evidence that background scenes modulated the search strategies adopted by participants, contrasting the results of Cosman and Vecera. Overall, the findings here support the conclusion that the visual system prioritizes task-relevant information while disregarding irrelevant background information.