Spatial transfer of object-based statistical learning

A large number of recent studies have demonstrated that efficient attentional selection depends to a large extent on the ability to extract regularities present in the environment. Through statistical learning, attentional selection is facilitated by directing attention to locations in space that were relevant in the past while suppressing locations that previously were distracting. The current study shows that we are not only able to learn to prioritize locations in space but also locations within objects independent of space. Participants learned that within a specific object, particular locations within the object were more likely to contain relevant information than other locations. The current results show that this learned prioritization was bound to the object as the learned bias to prioritize a specific location within the object stayed in place even when the object moved to a completely different location in space. We conclude that in addition to spatial attention prioritization of locations in space, it is also possible to learn to prioritize relevant locations within specific objects. The current findings have implications for the inferred spatial priority map of attentional weights as this map cannot be strictly retinotopically organized.

Neural evidence for attentional capture by salient distractors

Salient objects often capture our attention, serving as distractors and hindering our current goals. It remains unclear when and how salient distractors interact with our goals, and our knowledge on the neural mechanisms responsible for attentional capture is limited to a few brain regions recorded from non-human primates. Here we conducted a multivariate analysis on human intracranial signals covering most brain regions and successfully dissociated distractor-specific representations from target-arousal signals in the high-frequency (60-100 Hz) activity. We found that salient distractors were processed rapidly around 220 ms, while target-tuning attention was attenuated simultaneously, supporting initial capture by distractors. Notably, neuronal activity specific to the distractor representation was strongest in the superior and middle temporal gyrus, amygdala and anterior cingulate cortex, while there were smaller contributions from the parietal and frontal cortices. These results provide neural evidence for attentional capture by salient distractors engaging a much larger network than previously appreciated.

Reliability of individual differences in distractor suppression driven by statistical learning

A series of recent studies has demonstrated that attentional selection is modulated by statistical regularities, even when they concern task-irrelevant stimuli. Irrelevant distractors presented more frequently at one location interfere less with search than distractors presented elsewhere. To account for this finding, it has been proposed that through statistical learning, the frequent distractor location becomes suppressed relative to the other locations. Learned distractor suppression has mainly been studied at the group level, where individual differences are treated as unexplained error variance. Yet these individual differences may provide important mechanistic insights and could be predictive of cognitive and real-life outcomes. In the current study, we ask whether in an additional singleton task, the standard measures of attentional capture and learned suppression are reliable and stable at the level of the individual. In an online study, we assessed both the within- and between-session reliability of individual-level measures of attentional capture and learned suppression. We show that the measures of attentional capture, but not of distractor suppression, are moderately stable within the same session (i.e., split-half reliability). Test-retest reliability over a 2-month period was found to be moderate for attentional capture but weak or absent for suppression. RT-based measures proved to be superior to accuracy measures. While producing very robust findings at the group level, the predictive validity of these RT-based measures is still limited when it comes to individual-level performance. We discuss the implications for future research drawing on inter-individual variation in the attentional biases that result from statistical learning.

Statistical learning of motor preparation

Statistical learning, the process of extracting regularities from the environment, is one of the most fundamental abilities playing an essential role in almost all aspects of human cognition. Previous studies have shown that attentional selection is biased toward locations that are likely to contain a target and away from locations that are likely to contain a distractor. The current study investigated whether participants can also learn to extract that a specific motor response is more likely when the target is presented at specific locations within the visual field. To that end, the additional singleton paradigm was adapted such that when the singleton target was presented at one specific location, one response (e.g., right index finger) was more likely than the other (e.g., right middle finger) and the reverse was true for another location. The results show that participants learned to extract that a particular motor response is more likely when the singleton target (which was unrelated to the response) was presented at a specific location within the visual field. The results also suggest that it is the location of the target and not its shape that is associated with the biased response. This learning cannot be considered as being top-down or conscious as participants showed little, if any, awareness of the response biases present. The results are discussed in terms of the event coding theory. The study increases the scope of statistical learning and shows how individuals adapt automatically, without much awareness, to the regularities present in the environment. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Terms of debate: Consensus definitions to guide the scientific discourse on visual distraction

Hypothesis-driven research rests on clearly articulated scientific theories. The building blocks for communicating these theories are scientific terms. Obviously, communication - and thus, scientific progress - is hampered if the meaning of these terms varies idiosyncratically across (sub)fields and even across individual researchers within the same subfield. We have formed an international group of experts representing various theoretical stances with the goal to homogenize the use of the terms that are most relevant to fundamental research on visual distraction in visual search. Our discussions revealed striking heterogeneity and we had to invest much time and effort to increase our mutual understanding of each other's use of central terms, which turned out to be strongly related to our respective theoretical positions. We present the outcomes of these discussions in a glossary and provide some context in several essays. Specifically, we explicate how central terms are used in the distraction literature and consensually sharpen their definitions in order to enable communication across theoretical standpoints. Where applicable, we also explain how the respective constructs can be measured. We believe that this novel type of adversarial collaboration can serve as a model for other fields of psychological research that strive to build a solid groundwork for theorizing and communicating by establishing a common language. For the field of visual distraction, the present paper should facilitate communication across theoretical standpoints and may serve as an introduction and reference text for newcomers.

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.

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.

The Attentional Window, Search Difficulty and Search Modes: A Reply to Commentaries on Theeuwes (2023)

With great pleasure I studied the commentaries of my esteemed colleagues to my opinion paper "The Attentional Capture Debate: When Can We avoid Salient Distractors and When Not?" (Theeuwes, 2023). I thought the comments were to-the-point and provocative and I believe that these kinds of exchanges will help the field to move forward in this debate. I discuss the most pressing concerns in separate sections where I have grouped commonly raised issues.

The Attentional Capture Debate: When Can We Avoid Salient Distractors and When Not?

There has been a long-standing debate concerning whether we are able to resist attention capture by salient distractors. The so-called "signal suppression hypothesis" of Gaspelin and Luck (2018) claimed to have resolved this debate. According to this view, salient stimuli "naturally attempt to capture attention", yet attention capture may be prevented by a top-down inhibitory mechanism. The current paper describes the conditions in which attention capture by salient distractors can be avoided. Capture by salient items can be avoided when the target is non-salient and therefore difficult to find. Because fine discrimination is needed, a small attentional window is adapted resulting in serial (or partly serial) search. Salient signals outside the focused attentional window do not capture attention anymore not because they are suppressed but because they are ignored. We argue that in studies that have provided evidence for signal suppression, search was likely serial or at least partly serial. When the target is salient, search will be conducted in parallel, and in those cases the salient singleton cannot be ignored nor suppressed but instead will capture attention. We argue that the "signal suppression" account (Gaspelin & Luck, 2018) that seeks to explain resistance to attentional capture has many parallels to classic visual search models such as the "feature integration theory" (Treisman & Gelade, 1980), "feature inhibition" account (Treisman & Sato, 1990), and "guided search" (Wolfe et al, 1989); all models that explain how the serial deployment of attention is guided by the output of earlier parallel processes.

Readers use word length information to determine word order

It is assumed by the OB1-reader model that activated words are flexibly associated with spatial locations. Supporting this notion, recent studies show that readers can confuse the order of words. As word position coding is assumed to rely, among other things, on low-level visual cues, OB1 predicts that it must be harder to determine the order of words when these are of equal length, and consequently, that it is more difficult to read uniform word length sentences. Here we review recent evidence, obtained by our peers, in line with this prediction. We additionally report an analysis of eye-movement data from the GECO corpus, replicating the phenomenon in a natural reading setting, and an experiment revealing a negative impact of length uniformity in a grammatical decision task. By virtue of the spatiotopic sentence-level representation, OB1-reader is currently the only model of reading to account for these behaviors. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Attentional suppression is in place before display onset

Recent studies have shown that observers can learn to suppress a location that is most likely to contain a distractor. The current study investigates whether the statistically learned suppression is already in place, before, or implemented exactly at the moment participants expect the display to appear. Participants performed a visual search task in which a distractor was presented more frequently at the high-probability location (HPL) in a search display. Occasionally, the search display was replaced by a probe display in which participants needed to detect a probe offset. The temporal relationship between the probe display and the search display was manipulated by varying the stimulus onset asynchronies (SOAs) in the probe task. In this way, the attentional distribution in space was probed before, exactly at, or after the moment when the search display was expected to be presented. The results showed a statistically learned suppression at the HPL, as evidenced by faster and more accurate search when a distractor was presented at this location. Crucially, irrespective of the SOA, probe detection was always slower at the HPL than at the low-probability locations, indicating that the spatial suppression induced by statistical learning is proactively implemented not just at the moment the display is expected, but prior to display onset. We conclude that statistical learning affects the weights within the priority map relatively early in time, well before the availability of the search display.

Electrophysiological Indices of Distractor Processing in Visual Search Are Shaped by Target Expectations

Although in many cases salient stimuli capture attention involuntarily, it has been proposed recently that under certain conditions, the bottom-up signal generated by such stimuli can be proactively suppressed. In support of this signal suppression hypothesis, ERP studies have demonstrated that salient stimuli that do not capture attention elicit a distractor positivity (PD), a putative neural index of suppression. At the same time, it is becoming increasingly clear that regularities across preceding search episodes have a large influence on attentional selection. Yet to date, studies in support of the signal suppression hypothesis have largely ignored the role of selection history on the processing of distractors. The current study addressed this issue by examining how electrophysiological markers of attentional selection (N2pc) and suppression (PD) elicited by targets and distractors, respectively, were modulated when the search target randomly varied instead of being fixed across trials. Results showed that although target selection was unaffected by this manipulation, both in terms of manual response times, as well as in terms of the N2pc component, the PD component was reliably attenuated when the target features varied randomly across trials. This result demonstrates that the distractor PD, which is typically considered the marker of selective distractor processing, cannot unequivocally be attributed to suppression only, as it also, at least in part, reflects the upweighting of target features.

Statistical Learning Within Objects

Research has recently shown that efficient selection relies on the implicit extraction of environmental regularities, known as statistical learning. Although this has been demonstrated for scenes, similar learning arguably also occurs for objects. To test this, we developed a paradigm that allowed us to track attentional priority at specific object locations irrespective of the object's orientation in three experiments with young adults (all Ns = 80). Experiments 1a and 1b established within-object statistical learning by demonstrating increased attentional priority at relevant object parts (e.g., hammerhead). Experiment 2 extended this finding by demonstrating that learned priority generalized to viewpoints in which learning never took place. Together, these findings demonstrate that as a function of statistical learning, the visual system not only is able to tune attention relative to specific locations in space but also can develop preferential biases for specific parts of an object independently of the viewpoint of that object.

Long-term memory retrieval bypasses working memory

For decades, it has been assumed that when humans retrieve information from long-term memory (LTM), information need first to be brought back into working memory (WM). However, as WM capacity is limited, it is unclear what happens if information from LTM needs to be retrieved while WM is fully engaged? To address this question, observers had to retrieve colors from LTM while WM storage capacity was fully engaged. The behavioral results showed that retrieving information from LTM is possible even when WM capacity is fully occupied. Additional evidence from electroencephalogram (EEG) confirmed that WM was fully engaged as the suppression of alpha oscillation reached its maximum when memorizing the maximum amount of information into WM; yet the suppression in alpha oscillation was even further amplified when items were retrieved simultaneously from LTM, providing a neural signature of additional LTM retrieval capacity above and beyond the maximum WM capacity. Together, our findings indicate that information retrieved from LTM does not always have to be brought back into WM, but instead might be accessed through a different mechanism when WM is fully engaged.

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.

Ten simple rules to study distractor suppression

Distractor suppression refers to the ability to filter out distracting and task-irrelevant information. Distractor suppression is essential for survival and considered a key aspect of selective attention. Despite the recent and rapidly evolving literature on distractor suppression, we still know little about how the brain suppresses distracting information. What limits progress is that we lack mutually agreed upon principles of how to study the neural basis of distractor suppression and its manifestation in behavior. Here, we offer ten simple rules that we believe are fundamental when investigating distractor suppression. We provide guidelines on how to design conclusive experiments on distractor suppression (Rules 1-3), discuss different types of distractor suppression that need to be distinguished (Rules 4-6), and provide an overview of models of distractor suppression and considerations of how to evaluate distractor suppression statistically (Rules 7-10). Together, these rules provide a concise and comprehensive synopsis of promising advances in the field of distractor suppression. Following these rules will propel research on distractor suppression in important ways, not only by highlighting prominent issues to both new and more advanced researchers in the field, but also by facilitating communication between sub-disciplines.

Ten simple rules to study distractor suppression

Distractor suppression refers to the ability to filter out distracting and task-irrelevant information. Distractor suppression is essential for survival and considered a key aspect of selective attention. Despite the recent and rapidly evolving literature on distractor suppression, we still know little about how the brain suppresses distracting information. What limits progress is that we lack mutually agreed upon principles of how to study the neural basis of distractor suppression and its manifestation in behavior. Here, we offer ten simple rules that we believe are fundamental when investigating distractor suppression. We provide guidelines on how to design conclusive experiments on distractor suppression (Rules 1–3), discuss different types of distractor suppression that need to be distinguished (Rules 4–6), and provide an overview of models of distractor suppression and considerations of how to evaluate distractor suppression statistically (Rules 7–10). Together, these rules provide a concise and comprehensive synopsis of promising advances in the field of distractor suppression. Following these rules will propel research on distractor suppression in important ways, not only by highlighting prominent issues to both new and more advanced researchers in the field, but also by facilitating communication between sub-disciplines.

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Distractor suppression is the ability to filter out irrelevant information.

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At present, we know little about how the brain suppresses distraction.

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We offer ten rules that are fundamental when investigating distractor suppression.

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Following the rules will propel research and foster interaction between disciplines.

Proactive enhancement and suppression elicited by statistical regularities in visual search

The present study investigated how attentional selection is affected by simultaneous statistical learning of target and distractor regularities. Participants performed an additional singleton task in which the target singleton was presented more often in one location while the distractor singleton was presented more often in another location. On some trials, instead of the search task, participants performed a probe task, in which they had to detect the offset of a probe dot. This probe task made it possible to take a peek at the proactive selection priorities just at the moment the search display was presented. The results show that observers learn the regularities present in the search display such the location that is most likely to contain the target is enhanced while the location that is most likely to contain a distractor is suppressed. We show that these contingencies can be learned simultaneously resulting in optimal selection priorities. The probe task shows that both spatial enhancement and spatial suppression are present at the moment the actual search display is presented, indicating that the attentional priority settings are proactively modulated. We claim that through statistical learning the weights within the spatial priority map of selection are set in such a way that selection is optimally adapted to the implicitly learned regularities. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Statistical learning of across-trial regularities during serial search

Previous studies have shown that attention becomes biased toward those locations that frequently contain a target and is biased away from locations that have a high probability to contain a distractor. A recent study showed that participants also learned regularities that exist across trials: Participants were faster to find the singleton when its location was predicted by the location of the target singleton on the previous trial. Note, however, that this across-trial statistical learning was only demonstrated for parallel search involving "pop-out" singleton targets. The current study investigated whether there is also learning of across-trial regularities when search is serial, using a T-among-Ls task. In Experiment 1, using search displays with a gray T-target among gray Ls, we found that participants did not learn the existing across-trial regularities. In Experiment 2 we used the same display and same regularities except that during the first half of the experiment the targets were colored red, allowing feature search. Critically, now participants did learn the across-trial regularities during pop-out feature search and the learned biases persisted when search was serial again. Participants were not aware of these regularities suggesting that learning was automatic and implicit. We propose that across-trial target-target associations learned during feature search shape a flexible priority map whereby the selection of the predicting location results in up-weighting of the predicted location on the next trial. This flexible priority map remained active even when search task changed dramatically from parallel to serial search. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Does it help to expect distraction? Attentional capture is attenuated by high distractor frequency but not by trial-to-trial predictability

Salient distractors such as color singletons typically capture attention. Recent studies have shown that probabilistic expectations of color singletons' occurrence-even when their location and features are unpredictable-can eliminate attentional capture. Here we ask whether this effect, referred to as "second-order distractor suppression," (a) could be merely a result of repetition priming, and (b) is also observed when distractor occurrences are predictable within a sequence of trials? Experiment 1 introduces a novel approach for manipulating the frequency of distractor occurrence while controlling for intertrial priming by design, by embedding identical trial sequences in the to-be-compared conditions. We observed no elimination but significant attenuation of capture in the condition with a higher distractor frequency. In Experiments 2 and 3 we investigated the effect of the trial-to-trial predictability of distractor presence. Repeating regular distractor absent/present patterns did not result in attenuated capture compared with a random condition, not even when upcoming distractor presence was cued. Taken together, the results demonstrate that second-order distractor suppression is not merely a result of repetition priming. However, it is not a response to any type of expectation; this nonspecific type of suppression is almost instantly elicited by environments characterized by a high likelihood of distractors but not by distractor presence that can be anticipated on a trial-by-trial basis. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Enhancing banknote authentication by guiding attention to security features and manipulating prevalence expectancy

All banknotes have security features which are intended to help determine whether they are false or genuine. Typically, however, the general public has limited knowledge of where on a banknote these security features can be found. Here, we tested whether counterfeit detection can be improved with the help of salient elements, designed to guide bottom-up visuospatial attention. We also tested the influence of the participant's a priori level of trust in the authenticity of the banknote. In an online study (N = 422), a demographically diverse panel of Dutch participants distinguished genuine banknotes from banknotes with one (left- or right-sided) counterfeited security feature. Either normal banknotes (without novel design elements) or banknotes that contained a salient element (a pink rectangular frame) were presented for 1 s. To manipulate the participant's level of trust, trials were administered in three blocks, whereby at the start of each block, participants were instructed that either one third, one half, or two thirds of the upcoming banknotes were counterfeit (though the true ratio was always 1:1). We hypothesized (i) that in the presence of a salient element, counterfeits would be better detected when the location of the salient element aligned with the location of the counterfeited security feature-i.e. that it would act as an attentional cue; and (ii) that this effect would be stronger with lower trust. Our hypotheses were partly confirmed: counterfeit detection improved with 'valid cues' and decreasing trust, but the level of trust did not modulate the cueing effect. As the overall detection performance was rather poor, we replicated the study with a sample of university students (N = 66), this time presenting stimuli until response. While indeed observing better overall performance, all other patterns were replicated. Our results provide evidence that attention can be guided to enhance banknote authentication.

Statistical distractor learning modulates perceptual sensitivity

The present study used perceptual sensitivity (d′) to determine the spatial distribution of attention in displays in which participants have learned to suppress a location that is most likely to contain a distractor. Participants had to indicate whether a horizontal or a vertical line, which was shown only briefly before it was masked, was present within a target shape. Critically, the target shape could be accompanied by a singleton distractor color, which when present appeared with a high probability at one display location. The results show that perceptual sensitivity was reduced for locations likely to contain a distractor, as d′ was lower for this location than for all other locations in the display. We also found that the presence of an irrelevant color singleton reduced the gain for input at the target location, particularly when the irrelevant singleton was close to the target singleton. We conclude that, through the repeated encounter with a distractor at a particular location, the weights within the attentional priority map are changed such that the perceptual sensitivity for objects presented at that location is reduced relative to all other locations. This reduction of perceptual sensitivity signifies that this location competes less for attention than all other locations.

Attentional suppression in time and space

Distraction by a salient object can be reduced when we implicitly learn to suppress its most likely location. The current study investigated whether this suppression can also be tuned to the time at which the distractor is likely to appear. Participants performed the additional singleton task, in which they searched for a unique shape while a color singleton distractor was present. Following the fixation point, the search display was presented either after a short (500 ms) or long (1,500 ms) time interval. Critically, the color singleton distractor was presented relatively frequently at one high probability location after the short interval and at another high probability location after the long interval. The results showed that attentional capture at the two high probability locations was reduced relative to low probability distractor locations. More importantly, this reduction was greater when the color singleton distractor appeared at a high probability location after its associated interval than after the other interval. These findings indicate that participants learn to suppress particular locations at particular moments in time, suggesting that the spatial priority map of attentional selection is dynamically adjusted during the trial. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Self-explaining roads: What does visual cognition tell us about designing safer roads?

In 1995, Theeuwes and Godthelp published a paper called "self-explaining roads," in which they argued for the development of a new concept for approaching safe road design. Since this publication, self-explaining roads (SER) became one of the leading principles in road design worldwide. The underlying notion is that roads should be designed in such a way that road users immediately know how to behave and what to expect on these roads. In other words, the environment should be designed such that it elicits adequate and safe behavior. The present paper describes in detail the theoretical basis for the idea of SER and explains why this has such a large effect on human behavior. It is argued that the notion is firmly rooted in the theoretical framework of statistical learning, subjective road categorization and the associated expectations. The paper illustrates some successful implementation and describes recent developments worldwide.

Distractor suppression leads to reduced flanker interference

Recent studies using the additional singleton paradigm have shown that regularities in distractor locations can cause biases in the spatial priority map, such that attentional capture by salient singletons is reduced for locations that are likely to contain distractors. It has been suggested that this type of suppression is proactive (i.e., occurring before display onset). The current study replicated the original findings using an online version of the task. To further assess the suppression of high-probability locations, we employed a congruence manipulation similar to the traditional flanker effect, where distractors could be either congruent or incongruent with the response to the target. Experiment 1 shows that through statistical learning distractor suppression reduces the interference from incongruent distractors, as participants made less errors in high-probability versus low-probability conditions. In Experiment 2, participants were forced to search for a specific target feature (the so-called feature-search mode), which is assumed to allow participants to ignore distractors in a top-down manner. Yet even when this "top-down" search mode was employed, there was still a congruence effect when the distractor singleton was presented at the low-probability but not at the high-probability location. The absence, but not reversal, of a congruence effect at the high-probability location also further indicates that this distractor suppression mechanism is proactive. The results indicate that regardless of the search mode used, there is suppression of the high-probability location indicating that this location competes less for attention within the spatial priority map than all other locations.

Progress Toward Resolving the Attentional Capture Debate

For over 25 years, researchers have debated whether physically salient stimuli capture attention in an automatic manner, independent of the observer's goals, or whether the capture of attention depends on the match between a stimulus and the observer's task set. Recent evidence suggests an intermediate position in which salient stimuli automatically produce a priority signal, but the capture of attention can be prevented via an inhibitory mechanism that suppresses the salient stimulus. Here, proponents from multiple sides of the debate describe how their original views have changed in light of recent research, as well as remaining areas of disagreement. These perspectives highlight some emerging areas of consensus and provide new directions for future research on attentional capture.

Multivariate analysis of EEG activity indexes contingent attentional capture

An extensive body of work has shown that attentional capture is contingent on the goals of the observer: Capture is strongly reduced or even eliminated when an irrelevant singleton stimulus does not match the target-defining properties (Folk et al., 1992). There has been a long-standing debate on whether attentional capture can be explained by goal-driven and/or stimulus-driven accounts. Here, we shed further light on this matter by using EEG activity (raw EEG and alpha power) to provide a time-resolved index of attentional orienting towards salient stimuli that either matched or did not match target-defining properties. A search display containing the target stimulus was preceded by a spatially uninformative singleton cue that either matched the color of the upcoming target (contingent cues), or that appeared in an irrelevant color (non-contingent cues). Multivariate analysis of raw EEG and alpha power revealed preferential tuning to the location of both contingent and non-contingent cues, with a stronger bias towards contingent than non-contingent cues. The time course of these effects, however, depended on the neural signal. Raw EEG data revealed attentional orienting towards the contingent cue early on in the trial (>156 ms), while alpha power revealed sustained spatial selection in the cued locations at a later moment in the trial (>250 ms). Moreover, while raw EEG showed stronger capture by contingent cues during this early time window, an advantage for contingent cues arose during a later time window in alpha band activity. Thus, our findings suggest that raw EEG activity and alpha-band power tap into distinct neural processes that index separate aspects of covert spatial attention.

Statistical learning affects the time courses of salience-driven and goal-driven selection

The present study investigated how statistical regularities present in the display affected the time courses associated with salience-driven and goal-driven visual selection. In two experiments, participants were instructed to make a speeded saccade toward a prespecified target that was presented simultaneously with a distractor among multiple homogeneously oriented background lines. The distractor was presented more often at one location than at all other locations. We found that the statistical regularity regarding the distractor location affected visual selection very early, modulating the time courses associated with both salience-driven and goal-driven selection. These results suggest that statistical learning induces a continuous bias in visual selection, operating above and beyond salience-driven and goal-driven control. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Neural Dynamics of Reward-Induced Response Activation and Inhibition

Reward-predictive stimuli can increase an automatic response tendency, which needs to be counteracted by effortful response inhibition when this tendency is inappropriate for the current task. Here we investigated how the human brain implements this dynamic process by adopting a reward-modulated Simon task while acquiring EEG and fMRI data in separate sessions. In the Simon task, a lateral target stimulus triggers an automatic response tendency of the spatially corresponding hand, which needs to be overcome if the activated hand is opposite to what the task requires, thereby delaying the response. We associated high or low reward with different targets, the location of which could be congruent or incongruent with the correct response hand. High-reward targets elicited larger Simon effects than low-reward targets, suggesting an increase in the automatic response tendency induced by the stimulus location. This tendency was accompanied by modulations of the lateralized readiness potential over the motor cortex, and was inhibited soon after if the high-reward targets were incongruent with the correct response hand. Moreover, this process was accompanied by enhanced theta oscillations in medial frontal cortex and enhanced activity in a frontobasal ganglia network. With dynamical causal modeling, we further demonstrated that the connection from presupplementary motor area (pre-SMA) to right inferior frontal cortex (rIFC) played a crucial role in modulating the reward-modulated response inhibition. Our results support a dynamic neural model of reward-induced response activation and inhibition, and shed light on the neural communication between reward and cognitive control in generating adaptive behaviors.

Finding counterfeited banknotes: the roles of vision and touch

Central banks incorporate various security features in their banknotes to enable themselves, the general public, retailers and professional cash handlers to detect counterfeits. In two field experiments, we tested central bank counterfeit experts and non-experts (the general public) in their ability to detect counterfeited euro banknotes. We varied exposure duration and perceptual modality (sight, touch or both). The counterfeit banknotes were actual counterfeits taken out of circulation. Experiment 1, in which participants only viewed the banknotes, showed that experts did reasonably well in detecting counterfeits even when exposure duration was limited to 500 ms. Non-experts did not reach the criterion for decent performance, marked by d’ = 1.25, although they did perform above chance. In Experiment 2, participants could both see and touch the banknotes, which resulted in better performance especially with longer exposure durations. The main finding of the current study is that visual information mostly impacts the decision-making process during the first glance, whereas tactile information increasingly aids performance as it continues to be accrued over time. Implications for the design of security features of new banknotes are discussed.

Statistical learning in the absence of explicit top-down attention

Recently it has been shown that statistical learning of regularities presented in a display can bias attentional selection, such that attentional capture by salient objects is reduced by suppressing the location where these distractors are likely to appear. The role of attention in learning these contingencies is not immediately clear. Specifically, it is not known whether attention needs to be directed to the contingencies present in the display for learning to occur. In the current study we investigated whether participants can learn statistical regularities present in the environment even when these regularities are not relevant for the participant and are not part of their top-down goals. We used the additional singleton paradigm in which a color singleton was presented much more often in one location than in all other locations. We show that after being exposed to these regularities regarding the location of the color singleton during an unrelated task in which there are no targets nor distractors, participants showed a suppression effect from the previously learned contingencies when switching to a task in which they search for a target and suppress a distractor. We conclude that visual statistical learning can occur in the absence of top-down attention.

Proactively location-based suppression elicited by statistical learning

Recently, Wang and Theeuwes used the additional singleton task and showed that attentional capture was reduced for the location that was likely to contain a distractor [1]. It is argued that due to statistical learning, the location that was likely to contain a distractor was suppressed relative to all other locations. The current study replicated these findings and by adding a search-probe condition, we were able to determine the initial distribution of attentional resources across the visual field. Consistent with a space-based resource allocation ("biased competition") model, it was shown that the representation of a probe presented at the location that was likely to contain a distractor was suppressed relative to other locations. Critically, the suppression of this location resulted in more attention being allocated to the target location relative to a condition in which the distractor was not suppressed. This suggests that less capture by the distractor results in more attention being allocated to the target. The results are consistent with the view that the location that is likely to contain a distractor is suppressed before display onset, modulating the first feed-forward sweep of information input into the spatial priority map.

Implicit attentional biases in a changing environment

The current study investigates whether statistical regularities that change over time affect attentional selection. While searching for a target singleton, the distractor singleton was presented much more often in one location than in all other locations. Crucially, the location that had a distractor much more often, changed to new locations during the course of the experiment. Here we established exactly how the bias of attention followed these changes in the display. Unlike previous studies, we show that selection was remarkably flexible as the attentional bias followed the changes in the environment incorporating contributions of previous contingencies to the current attentional bias. Importantly, the initial learning experience had a lingering and enduring effect on subsequent attentional biases. We argue that the weights within the spatial priority map of selection are adjusted to changing environments, even though observers are unaware of these changes in the environment.

Statistical regularities across trials bias attentional selection

Previous studies have shown that attentional selection can be biased toward locations that are likely to contain a target and away from locations that are likely to contain a distractor. It is assumed that through statistical learning, participants are able to extract the regularities in the display, which in turn biases attentional selection. The present study employed the additional singleton task to examine the ability of participants to extract regularities that occurred across trials. In four experiments, we found that participants were capable of picking up statistical regularities concerning target positions across trials both in the absence and presence of distracting information. It is concluded that through statistical learning, participants are able to extract intertrial statistical associations regarding subsequent target location, which in turn biases attentional selection. We argue here that the weights within the spatial priority map can be dynamically adapted from trial to trial such that the selection of a target at a particular location increases the weights of the upcoming target location within the spatial priority map, giving rise to a more efficient target selection. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Learning in Visual Regions as Support for the Bias in Future Value-Driven Choice

Reinforcement learning can bias decision-making toward the option with the highest expected outcome. Cognitive learning theories associate this bias with the constant tracking of stimulus values and the evaluation of choice outcomes in the striatum and prefrontal cortex. Decisions however first require processing of sensory input, and to date, we know far less about the interplay between learning and perception. This functional magnetic resonance imaging study (N = 43) relates visual blood oxygen level-dependent (BOLD) responses to value beliefs during choice and signed prediction errors after outcomes. To understand these relationships, which co-occurred in the striatum, we sought relevance by evaluating the prediction of future value-based decisions in a separate transfer phase where learning was already established. We decoded choice outcomes with a 70% accuracy with a supervised machine learning algorithm that was given trial-by-trial BOLD from visual regions alongside more traditional motor, prefrontal, and striatal regions. Importantly, this decoding of future value-driven choice outcomes again highlighted an important role for visual activity. These results raise the intriguing possibility that the tracking of value in visual cortex is supportive for the striatal bias toward the more valued option in future choice.

Attentional capture by Pavlovian reward-signalling distractors in visual search persists when rewards are removed

Existing research indicates that learning about the Pavlovian ‘signal value’ of stimuli can induce attentional biases: findings suggest that our attentional system prioritises detection of stimuli that have previously signalled availability of high reward. These findings potentially provide a human analogue of sign-tracking behaviour previously reported in studies of non-human animals. Here we examine a visual search task that has been developed to demonstrate the Pavlovian influence of reward on attention, in which the critical reward-signalling stimuli are never explicit targets of search. This procedure has previously yielded robust effects of reward on attention; however it remains unclear whether this pattern reflects a persistent and automatic bias in attentional capture based on prior experience of stimulus–reward pairings, or whether it results from participants strategically attending to reward-signalling distractors because they provide useful information about reward magnitude. To investigate this issue, in the current study participants initially completed a rewarded visual search task, in which colours of distractor stimuli signalled availability of high or low reward. Participants then completed a test phase in which rewards were no longer available, such that distractor colours no longer provided useful information on reward availability. Performance during the initial rewarded phase was impaired by the presence of a distractor signalling availability of high relative to low reward. Crucially, the magnitude of this reward-related distraction effect did not reduce in the subsequent unrewarded test phase. This suggests that participants’ experience of differences in reward value signalled by distractor stimuli in this task can induce persistent biases in the extent to which these stimuli involuntarily capture attention, even when they are entirely task-irrelevant.