Implicit attentional biases in a changing environment

The self-relevant spotlight metaphor: Self-relevant targets diminish distractor-response-binding effects

Recently, it has been proposed that self-relevance of a stimulus enhances executive control and reduces the impact of distractors on current task performance. The present study aimed to test whether the binding between a distractor and a response is influenced by self-relevance, too. We assumed that targets' self-relevance should increase executive control processes and therefore reduce the influence of distractors on current performance. In a distractor-response-binding (DRB) task, which measures the strength of binding between distractor stimuli and responses, we varied target relevance so that participants responded to targets that either were or were not self-relevant. Our design made it possible to measure DRB effects for both relevance conditions separately. DRB effects were diminished if targets were self-relevant compared to when they were not. These results expand our understanding of the influence of self-relevance on cognitive performance. The influence of self-relevance is not purely perceptual (Sui & Humphreys, 2012, Journal of Experimental Psychology: Human Perception and Performance, 38[5], 1105-1117), but also found in higher-order processes such as executive control. Moreover, whereas for different paradigms binding advantages of self-relevance are assumed (Sui & Humphreys, 2015a, Trends in Cognitive Sciences, 19[12], 719-728; Humphreys & Sui, 2016, Cognitive Neuroscience, 7[1/4], 5-17), this study identifies an important boundary condition, in that distractor-response binding is reduced by target self-relevance.

Statistical learning re-shapes the center-surround inhibition of the visuo-spatial attentional focus

To effectively navigate a crowded and dynamic visual world, our neurocognitive system possesses the remarkable ability to extract and learn its statistical regularities to implicitly guide the allocation of spatial attention resources in the immediate future. The way through which we deploy attention in the visual space has been consistently outlined by a "center-surround inhibition" pattern, wherein a ring of sustained inhibition is projected around the center of the attentional focus to optimize the signal-noise ratio between goal-relevant targets and interfering distractors. While it has been observed that experience-dependent mechanisms could disrupt the inhibitory ring, whether statistical learning of spatial contingencies has an effect on such a surround inhibition and - if any - through which exact mechanisms it unravels are hitherto unexplored questions. Therefore, in a visual search psychophysical experiment, we aimed to fill this gap by entirely mapping the visuo-spatial attentional profile, asking subjects (N = 26) to detect and report the gap orientation of a 'C' letter appearing either as a color singleton (Baseline Condition) or as a non-salient probe (Probe Condition) - among other irrelevant objects - at progressively increasing probe-to-singleton distances. Critically, we manipulated the color singleton spatial contingency so as to make it appear more frequently adjacent to the probe, specifically at a spatial distance where attending the color singleton generates surround-inhibition on the probe, hindering attentional performance. Results showed that statistical learning markedly reshaped the attentional focus, transforming the center-surround inhibition profile into a non-linear gradient one through a performance gain over the high probability probe-to-singleton distance. Noteworthy, such reshaping was uneven in time and asymmetric, as it varied across blocks and specifically appeared only within manipulated visual quadrants, leaving unaltered the unmanipulated ones. Our findings offer insights of theoretical interest in understanding how environmental regularities orchestrate the way we allocate attention in space through plastic re-weighting of spatial priority maps. Additionally, going beyond the physical dimension, our data provide interesting implications about how visual information is coded within working memory representations, especially under scenarios of heightened uncertainty.

Learning of the mean, but not variance, of color distributions cues target location probability

Humans are good at picking up statistical regularities in the environment. Probability cueing paradigms have demonstrated that the location of a target can be predicted based on spatial regularities. This is assumed to rely on flexible spatial priority maps that are influenced by visual context. We investigated whether stimulus features such as color distributions differing in mean and variance can cue location regularities. In experiment 1, participants searched for an oddly colored target diamond in a 6 × 6 set. On each trial, the distractors were drawn from one of two color distributions centered on different color averages. Each distribution was associated with different target location probabilities, one distribution where the target had an 80% chance to appear on the left (the rich location), while the rich location would be on the right for the other distribution. Participants were significantly faster at locating the target when it appeared in the rich location for both distributions, demonstrating learning of the relationship between color average and location probability. In experiments 2 and 3, observers performed a similar search task, but the distributions had different variances with the same average color. There was no evidence that search became faster when the target appeared in a rich location, suggesting that contingencies between target probabilities and color variance were not learned. These results demonstrate how statistical location learning is flexible, with different visual contexts leading to different spatial priority maps, but they also reveal important limits to such learning.

Neural mechanisms of learned suppression uncovered by probing the hidden attentional priority map

Attentional capture by an irrelevant salient distractor is attenuated when the distractor appears more frequently in one location, suggesting learned suppression of that location. However, it remains unclear whether suppression is proactive (before attention is directed) or reactive (after attention is allocated). Here, we investigated this using a 'pinging' technique to probe the attentional distribution before search onset. In an EEG experiment, participants searched for a shape singleton while ignoring a color singleton distractor at a high-probability location. To reveal the hidden attentional priority map, participants also performed a continuous recall spatial memory task, with a neutral placeholder display presented before search onset. Behaviorally, search was more efficient when the distractor appeared at the high-probability location. Inverted encoding analysis of EEG data showed tuning profiles that decayed during memory maintenance but were revived by the placeholder display. Notably, tuning was most pronounced at the to-be-suppressed location, suggesting initial spatial selection followed by suppression. These findings suggest that learned distractor suppression is a reactive process, providing new insights into learned spatial distractor suppression mechanisms.

Attentional Capture and Control

The current review presents an integrated tripartite framework for understanding attentional control, emphasizing the interaction and competition among top-down, bottom-up, and selection-history influences. It focuses on attentional capture, which refers to conditions in which salient objects or events receive attentional priority even when they are inconsistent with the goals, tasks, and intentions of the observer. The review describes which components of the tripartite framework are in play when distraction by salient objects is prevented and the conditions in which there is no control over the occurrence of attentional capture. It then concludes that attentional capture can be controlled in a proactive way mainly by implicit statistical learning mechanisms associated with selection history. Current and lingering controversies regarding the control of attentional capture are also discussed.

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 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.

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.

Target templates and the time course of distractor location learning

When searching for a shape target, colour distractors typically capture our attention. Capture is smaller when observers search for a fixed target that allows for a feature-specific target template compared to a varying shape singleton target. Capture is also reduced when observers learn to predict the likely distractor location. We investigated how the precision of the target template modulates distractor location learning in an additional singleton search task. As observers are less prone to capture with a feature-specific target, we assumed that distractor location learning is less beneficial and therefore less pronounced than with a mixed-feature target. Hierarchical Bayesian parameter estimation was used to fit fine-grained distractor location learning curves. A model-based analysis of the time course of distractor location learning revealed an effect on the asymptotic performance level: when searching for a fixed-feature target, the asymptotic distractor cost indicated smaller distractor interference than with a mixed-feature target. Although interference was reduced for distractors at the high-probability location in both tasks, asymptotic distractor suppression was less pronounced with fixed-feature compared to mixed-feature targets. We conclude that with a more precise target template less distractor location learning is required, likely because the distractor dimension is down-weighted and its salience signal reduced.

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.

Spatial enhancement due to statistical learning tracks the estimated spatial probability

It is well known that attentional selection is sensitive to the regularities presented in the display. In the current study we employed the additional singleton paradigm and systematically manipulated the probability that the target would be presented in one particular location within the display (probabilities of 30%, 40%, 50%, 60%, 70%, 80%, and 90%). The results showed the higher the target probability, the larger the performance benefit for high- relative to low-probability locations both when a distractor was present and when it was absent. We also showed that when the difference between high- and low-probability conditions was relatively small (30%) participants were not able to learn the contingencies. The distractor presented at a high-probability target location caused more interference than when presented at a low-probability target location. Overall, the results suggest that attentional biases are optimized to the regularities presented in the display tracking the experienced probabilities of the locations that were most likely to contain a target. We argue that this effect is not strategic in nature nor the result of repetition priming. Instead, we assume that through statistical learning the weights within the spatial priority map are adjusted optimally, generating the efficient selection priorities.

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.

Local and global context repetitions in contextual cueing

In contextual cueing tasks, participants can use a repeating local context to learn to detect the target, yet most contextual cueing studies have relied on repeating global context properties. We examined whether observers can use local context repetitions in a similar manner as they use global context repetitions. In addition, we examined how reward-predicting context features modulate the use of local and global contexts. Participants searched through contexts in which either the entire context configuration or only a local context around the target repeated, intermixed with novel contexts. Half of the context items appeared in a color signaling either low or high reward. We found that local context repetitions led to comparable benefits in response times and fixation count as global context repetitions did. Surprisingly, reward magnitude did not affect performance in local nor in global contexts. The results suggest that a local chunk of distractors can be used for context learning and attention guidance in a similar manner as the global context configuration. We suggest that the proportion of repeated and novel context trials is crucial for context learning and that our combination of locally and globally repeating contexts provided an environment that facilitated learning in both context types because it allowed predicting the target location from the context in most of the trials.

Across-trial spatial suppression in visual search

In order to focus on objects of interest, humans must be able to avoid distraction by salient stimuli that are not relevant to the task at hand. Many recent studies have shown that through statistical learning we are able to suppress the location that is most likely to contain a salient distractor. Here we demonstrate a remarkable flexibility in attentional suppression. Participants had to search for a shape singleton while a color distractor singleton was present. Unbeknown to the participant, the color distractor was presented according to a consistent pattern across trials. Our findings show that participants learn this distractor sequence as they proactively suppressed the anticipated location of the distractor on the next trial. Critically, none of the participants were aware of these hidden sequences. We conclude that the spatial priority map is highly flexible, operating at a subconscious level preparing the attentional system for what will happen next.

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.

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.

What Employees Do Today Because of Their Experience Yesterday: How Incidental Learning Influences Train Driver Behavior and Safety Margins (A Big Data Analysis)

Employee behavior plays an important role in the occurrence and prevention of incidents, affecting safety margins. In this study, we examine the potential impact of incidental learning on human behavior in the presence of variation in task design. Incidental learning is the day-to-day on-the-job learning that occurs unintentionally. This learning influences which behavior (schema) is more likely to be activated in the employee’s brain. We posit that an incorrect schema can be activated and lead to undesired behavior if the employee is often exposed to (visually) similar tasks that require different behavior. In rail transport, there is a risk of trains passing through red signals. The train driver’s behavior plays an important role in preventing these signal passed at danger (SPAD) incidents. In this study we used speed and location data to analyze train driver deceleration behavior during red signal approaches in the Netherlands. The Dutch rail system showed variation in yellow signal aspects and signal distance. An analysis using 19 months of empirical data indicated changes in behavior when the employee had been previously exposed to different behavior requirements in the same location with a similar yellow signal. These results imply that task design can be improved by taking into consideration what an employee is exposed to during other moments of the shift, and not just during the execution of the specific task.

Spatial suppression due to statistical learning tracks the estimated spatial probability

People are sensitive to regularities in the environment. Recent studies employing the additional singleton paradigm showed that a singleton distractor that appeared more often in one specific location than in all other locations may lead to attentional suppression of high-probability distractor locations. This in turn effectively reduced the attentional capture effect by the salient distractor singleton. However, in basically all of these previous studies, the probability that the salient distractor was presented at this specific location was relatively high (i.e., 65%; or a ratio of 13:1 between high- and low-probability locations). The question we addressed here was whether participants still can learn the regularities in the display even when these regularities are quite subtle. We systematically manipulated the ratio of the distractor appearing at the high- and low-probability location from 2:1 to 8:1. We asked the question whether the suppression effect would depend on the probabilities of the distractor appearing in the high-probability location. The results showed that the suppression of the high-probability location was linearly related to the high-low-probability ratio. In other words, the more evidence that a distractor appears more often at a particular location, the stronger the suppression. This indicates that the distribution of attention is optimally adapted to the statistical regularities present in the display.

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.