The adaptive global effect: Luminance contrast modulates the global effect zone.

When two peripheral objects are presented in close proximity, saccades towards one of these objects land at a weighted average location between the two objects. This phenomenon, known as the 'global effect' or 'saccade averaging', disappears when the distance between the objects increases. When objects are further apart, outside the averaging zone, saccades land on one of the objects with little or no saccade averaging. Although it is known that the strength of the global effect is dependent on the specific features of the two objects, it is unclear if the size of the zone in which averaging can occur (i.e., the averaging zone) is adaptive. The aim of the current study was to investigate whether the size of the averaging zone adapts to variations in object luminance contrast of the objects. In order to systematically assess changes in the averaging zone, in two experiments, observers made saccadic eye movements while the luminance of the target and the distractor varied. We report three major findings: 1) When a distractor was more luminant relative to the target, the averaging zone increased (Exp. 1). Notably, saccade averaging never entirely ceased to exist, even for remote distractors. 2) When target and distractor were equiluminant, the averaging zone did not change with absolute luminance (Exp. 2). 3) Higher (relative and absolute) luminance increased the averaging zone especially for shorter saccadic response times (SRT). We conclude that the averaging zone is adaptive and becomes larger with increasing relative luminance and especially when SRTs are short.

Uncertainty-modulated attentional capture: Outcome variance increases attentional priority.

Our prior experiences shape the way that we prioritize information from the environment for further processing, analysis, and action. We show in three experiments that this process of attentional prioritization is critically modulated by the degree of uncertainty in these previous experiences. Participants completed a visual search task in which they made a saccade to a target to earn a monetary reward. The color of a color-singleton distractor in the search array signaled the reward outcome(s) that were available, with different degrees of variance (uncertainty). Participants were never required to look at the colored distractor, and doing so would slow their response to the target. Nevertheless, across all experiments, participants were more likely to look at distractors associated with high outcome variance versus low outcome variance. This pattern was observed when all distractors had equal expected value (Experiment 1), when the difference in variance was opposed by a difference in expected value (i.e., the high-variance distractor had a low expected value, and vice versa: Experiment 2), and when high- and low-variance distractors were paired with the maximum-value outcome on an equal proportion of trials (Experiment 3). Our findings demonstrate that experience of prediction error plays a fundamental role in guiding "attentional exploration," wherein priority is driven by the potential for a stimulus to reduce future uncertainty through a process of learning, as opposed to maximizing current information gain. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Transfer of statistical learning between tasks.

Recent studies have shown that observers can learn to suppress locations in the visual field with a high distractor probability. Here, we investigated whether this learned suppression resulting from a spatial distractor imbalance transfers to a completely different search task that does not contain any distractors. Observers performed the additional singleton task and learned to suppress the location that was likely to contain a color singleton distractor. Within a block, the additional singleton task would randomly switch to a T-among-L task where observers searched in parallel (Experiment 1) or serially (Experiment 2) for a T among Ls. The upcoming search was either unpredictable (Experiment 1/2A) or cued (Experiment 1/2B). The results show that there was transfer of learning from one to the other task as the learned suppression stayed in place after the switch regardless of whether the T-among-L task was performed via parallel or serial search. Moreover, cueing that the task would switch had no effect on performance. The current findings indicate that implicit learned biases are rather inflexible and remain in place even when the task and the required search strategy are dramatically different and even when participants can anticipate that a change in the search required is imminent. This transfer of the suppression to a different task is consistent with the notion that suppression is proactively applied. Because the location is already suppressed proactively, that is, before display onset, regardless which display and task is presented, the suppressed location competes less for attention than all other locations. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Salient distractor processing: inhibition following attentional capture.

Salient objects often capture attention in a purely exogenous way, followed by inhibition of their locations after a period. Yet, the neural circuits underlying the exogenous attention remain underspecified. Seidel Malkinson et al. explore this by uncovering large-scale cortical gradients associated with exogenous attention within the human cortex.

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.

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.

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.

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.

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.

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.

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.

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.

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).

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.

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.

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.

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.

No evidence for spatial suppression due to across-trial distractor learning in visual search.

Previous studies have shown that during visual search, participants are able to implicitly learn across-trial regularities regarding target locations and use these to improve search performance. The present study asks whether such across-trial visual statistical learning also extends to the location of salient distractors. In Experiments 1 and 2, distractor regularities were paired so that a specific distractor location was 100% predictive of another specific distractor location on the next trial. Unlike previous findings that employed target regularities, the current results show no difference in search times between predictable and unpredictable trials. In Experiments 3-5 the distractor location was presented in a structured order (a sequence) for one group of participants, while it was presented randomly for the other group. Again, there was no learning effect of the across-trial regularities regarding the salient distractor locations. Across five experiments, we demonstrated that participants were unable to exploit across-trial spatial regularities regarding the salient distractors. These findings point to important boundary conditions for the modulation of visual attention by statistical regularities and they highlight the need to differentiate between different types of statistical regularities.