Statistical learning in the absence of explicit top-down attention

Statistical Learning of Incidental Perceptual Regularities Induces Sensory Conditioned Cortical Responses

Statistical learning of sensory patterns can lead to predictive neural processes enhancing stimulus perception and enabling fast deviancy detection. Predictive processes have been extensively demonstrated when environmental statistical regularities are relevant to task execution. Preliminary evidence indicates that statistical learning can even occur independently of task relevance and top-down attention, although the temporal profile and neural mechanisms underlying sensory predictions and error signals induced by statistical learning of incidental sensory regularities remain unclear. In our study, we adopted an implicit sensory conditioning paradigm that elicited the generation of specific perceptual priors in relation to task-irrelevant audio-visual associations, while recording Electroencephalography (EEG). Our results showed that learning task-irrelevant associations between audio-visual stimuli resulted in anticipatory neural responses to predictive auditory stimuli conveying anticipatory signals of expected visual stimulus presence or absence. Moreover, we observed specific modulation of cortical responses to probabilistic visual stimulus presentation or omission. Pattern similarity analysis indicated that predictive auditory stimuli tended to resemble the response to expected visual stimulus presence or absence. Remarkably, Hierarchical Gaussian filter modeling estimating dynamic changes of prediction error signals in relation to differential probabilistic occurrences of audio-visual stimuli further demonstrated instantiation of predictive neural signals by showing distinct neural processing of prediction error in relation to violation of expected visual stimulus presence or absence. Overall, our findings indicated that statistical learning of non-salient and task-irrelevant perceptual regularities could induce the generation of neural priors at the time of predictive stimulus presentation, possibly conveying sensory-specific information about the predicted consecutive stimulus.

What is the Role of Spatial Attention in Statistical Learning During Visual Search?

Our ability to learn the regularities embedded in our environment is a fundamental aspect of our cognitive system. Does such statistical learning depend on attention? Research on this topic is scarce and has yielded mixed findings. In this preregistered study, we examined the role of spatial attention in statistical learning, and specifically in learned distractor-location suppression. This phenomenon refers to the finding that during visual search, participants are better at ignoring a salient distractor at a high-probability location than at low-probability locations - a bias persisting long after the probability imbalance has ceased. Participants searched for a shape-singleton target and a color-singleton distractor was sometimes present. During the learning phase, the color-singleton distractor was more likely to appear in the high-probability location than in the low-probability locations. Crucially, we manipulated spatial attention by having the experimental group focus their attention on the target's location in advance of the search display, using a 100%-informative spatial precue, while the control group was presented with a neutral, uninformative cue. During the subsequent test phase, the color-singleton distractor was equally likely to appear at any location and there were no cues. As expected, the results for the neutral-cue group replicated previous findings. Crucially, for the informative-cue group, interference from the distractor was minimal when attention was diverted from it (during learning) and no statistical learning was observed during test. Intertrial priming accounted for the small statistical-learning effect found during learning. These findings show that statistical learning in visual search requires attention.

The Brain's Sensitivity to Real-world Statistical Regularity Does Not Require Full Attention

Predictive coding accounts of perception state that the brain generates perceptual predictions in the service of processing incoming sensory data. These predictions are hypothesized to be afforded by the brain's ability to internalize useful patterns, that is, statistical regularities, from the environment. We have previously argued that the N300 ERP component serves as an index of the brain's use of representations of (real-world) statistical regularities. However, we do not yet know whether overt attention is necessary in order for this process to engage. We addressed this question by presenting stimuli of either high or low real-world statistical regularity in terms of their representativeness (good/bad exemplars of natural scene categories) to participants who either fully attended the stimuli or were distracted by another task (attended/distracted conditions). Replicating past work, N300 responses were larger to bad than to good scene exemplars, and furthermore, we demonstrate minimal impacts of distraction on N300 effects. Thus, it seems that overtly focused attention is not required to maintain the brain's sensitivity to real-world statistical regularity. Furthermore, in an exploratory analysis, we showed that providing additional, artificial regularities, formed by altering the proportions of good and bad exemplars within blocks, further enhanced the N300 effect in both attended and distracted conditions, shedding light on the relationship between statistical regularities learned in the real world and those learned within the context of an experiment.

Is attention necessary for the representational advantage of good exemplars over bad exemplars?

Real-world (rw-) statistical regularities, or expectations about the visual world learned over a lifetime, have been found to be associated with scene perception efficiency. For example, good (i.e., highly representative) exemplars of basic scene categories, one example of an rw-statistical regularity, are detected more readily than bad exemplars of the category. Similarly, good exemplars achieve higher multivariate pattern analysis (MVPA) classification accuracy than bad exemplars in scene-responsive regions of interest, particularly in the parahippocampal place area (PPA). However, it is unclear whether the good exemplar advantages observed depend on or are even confounded by selective attention. Here, we ask whether the observed neural advantage of the good scene exemplars requires full attention. We used a dual-task paradigm to manipulate attention and exemplar representativeness while recording neural responses with functional magnetic resonance imaging (fMRI). Both univariate analysis and MVPA were adopted to examine the effect of representativeness. In the attend-to-scenes condition, our results replicated an earlier study showing that good exemplars evoke less activity but a clearer category representation than bad exemplars. Importantly, similar advantages of the good exemplars were also observed when participants were distracted by a serial visual search task demanding a high attention load. In addition, cross-decoding between attended and distracted representations revealed that attention resulted in a quantitative (increased activation) rather than qualitative (altered activity patterns) improvement of the category representation, particularly for good exemplars. We, therefore, conclude that the effect of category representativeness on neural representations does not require full attention.

Does animacy affect visual statistical learning? Revisiting the effects of selective attention and animacy on visual statistical learning

Animates receive preferential attentional processing over inanimates because, from an evolutionary perspective, animates are important to human survival. We investigated whether animacy affects visual statistical learning-the detection and extraction of regularities in visual information from our rich, dynamic, and complex environment. Participants completed a selective-attention task, in which regularities were embedded in two visual streams, an attended and an unattended visual stream. The attended visual stream always consisted of line-drawings of non-objects, while the unattended visual stream consisted of line-drawings of either animates or inanimates. Participants then completed a triplet-discrimination task, which assessed their ability to extract regularities from the attended and unattended visual streams. We also assessed participants' awareness of regularities in the visual statistical learning task, and asked if any learning strategies were used. We were specifically interested in whether the animacy status of line-drawings in the unattended visual stream would affect visual statistical learning. There were four key findings. First, selective attention modulates visual statistical learning, with greater visual statistical learning for attended than for unattended information. Second, animacy does not affect visual statistical learning, with no differences found in visual statistical learning performance between the animate and inanimate condition. Third, awareness of regularities was associated with visual statistical learning of attended information. Fourth, participants used strategies (e.g., naming or labelling stimuli) during the visual statistical learning task. Further research is required to understand whether visual statistical learning is one of the adaptive functions that evolved from ancestral environments.

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.

Beat processing in newborn infants cannot be explained by statistical learning based on transition probabilities

Newborn infants have been shown to extract temporal regularities from sound sequences, both in the form of learning regular sequential properties, and extracting periodicity in the input, commonly referred to as a regular pulse or the 'beat'. However, these two types of regularities are often indistinguishable in isochronous sequences, as both statistical learning and beat perception can be elicited by the regular alternation of accented and unaccented sounds. Here, we manipulated the isochrony of sound sequences in order to disentangle statistical learning from beat perception in sleeping newborn infants in an EEG experiment, as previously done in adults and macaque monkeys. We used a binary accented sequence that induces a beat when presented with isochronous timing, but not when presented with randomly jittered timing. We compared mismatch responses to infrequent deviants falling on either accented or unaccented (i.e., odd and even) positions. Results showed a clear difference between metrical positions in the isochronous sequence, but not in the equivalent jittered sequence. This suggests that beat processing is present in newborns. Despite previous evidence for statistical learning in newborns the effects of this ability were not detected in the jittered condition. These results show that statistical learning by itself does not fully explain beat processing in newborn infants.

Investigating the role of spatial filtering on distractor suppression

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

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

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

Learning to suppress a location is configuration-dependent

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

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.

Updating constructions: additive effects of prior and current experience during sentence production

While much earlier work has indicated that prior verb bias from lifelong language experience influences language processing, recent findings highlight the fact that verb biases induced during lab-based exposure sessions also influence processing. We investigated the nature of updating, i.e., how prior and current experience might interact in guiding subsequent sentence production. Participants underwent a short training session where we manipulated the bias of known English dative verbs. The prior bias of each verb for the double-object (DO) versus the prepositional-object (PO) dative was estimated using a corpus. Current verb bias was counterbalanced and controlled experimentally. Bayesian mixed-effects logistic models of participants' responses (DO or PO) during subsequent free-choice production showed that both the prior and current verb biases affected speakers' construction choice. These effects were additive and not interactive, contrary to the prediction from error-based learning models. Semantic similarity to other verbs and their experimentally manipulated biases influenced sentence production, consistent with item-based analogy and exemplar theory. These results shed light on the potential mechanisms underlying language updating and the adaptation of sentence production to ongoing experience.

Integrated effects of top-down attention and statistical learning during visual search: An EEG study

The present study aims to investigate how the competition between visual elements is solved by top-down and/or statistical learning (SL) attentional control (AC) mechanisms when active together. We hypothesized that the "winner" element that will undergo further processing is selected either by one AC mechanism that prevails over the other, or by the joint activity of both mechanisms. To test these hypotheses, we conducted a visual search experiment that combined an endogenous cueing protocol (valid vs. neutral cue) and an imbalance of target frequency distribution across locations (high- vs. low-frequency location). The unique and combined effects of top-down control and SL mechanisms were measured on behaviour and amplitudes of three evoked-response potential (ERP) components (i.e., N2pc, P1, CNV) related to attentional processing. Our behavioural results showed better performance for validly cued targets and for targets in the high-frequency location. The two factors were found to interact, so that SL effects emerged only in the absence of top-down guidance. Whereas the CNV and P1 only displayed a main effect of cueing, for the N2pc we observed an interaction between cueing and SL, revealing a cueing effect for targets in the low-frequency condition, but not in the high-frequency condition. Thus, our data support the view that top-down control and SL work in a conjoint, integrated manner during target selection. In particular, SL mechanisms are reduced or even absent when a fully reliable top-down guidance of attention is at play.

The effect of load on spatial statistical learning

Statistical learning (SL), the extraction of regularities embedded in the environment, is often viewed as a fundamental and effortless process. However, whether spatial SL requires resources, or it can operate in parallel to other demands, is still not clear. To examine this issue, we tested spatial SL using the standard lab experiment under concurrent demands: high- and low-cognitive load (Experiment 1) and, spatial memory load (Experiment 2) during the familiarization phase. We found that any type of high-load demands during the familiarization abolished learning. Experiment 3 compared SL under spatial low-load and no-load. We found robust learning in the no-load condition that was dramatically reduced in the low-load condition. Finally, we compared a no-load condition with a very low-load, infrequent dot-probe condition that posed minimal demands while still requiring attention to the display (Experiment 4). The results showed, once again, that any concurrent task during the familiarization phase largely impaired spatial SL. Taken together, we conclude that spatial SL requires resources, a finding that challenges the view that the extraction of spatial regularities is automatic and implicit and suggests that this fundamental learning process is not as effortless as was typically assumed. We further discuss the practical and methodological implications of these findings.

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.

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.

No reliable effect of task-irrelevant cross-modal statistical regularities on distractor suppression

Our sensory systems are known to extract and utilize statistical regularities in sensory inputs across space and time for efficient perceptual processing. Past research has shown that participants can utilize statistical regularities of target and distractor stimuli independently within a modality either to enhance the target or to suppress the distractor processing. Utilizing statistical regularities of task-irrelevant stimuli across different modalities also enhances target processing. However, it is not known whether distractor processing can also be suppressed by utilizing statistical regularities of task-irrelevant stimulus of different modalities. In the present study, we investigated whether the spatial (Experiment 1) and non-spatial (Experiment 2) statistical regularities of task-irrelevant auditory stimulus could suppress the salient visual distractor. We used an additional singleton visual search task with two high-probability colour singleton distractor locations. Critically, the spatial location of the high-probability distractor was either predictive (valid trials) or unpredictive (invalid trials) based on the statistical regularities of the task-irrelevant auditory stimulus. The results replicated earlier findings of distractor suppression at high-probability locations compared to the locations where distractors appear with lower probability. However, the results did not show any RT advantage for valid distractor location trials as compared with invalid distractor location trials in both experiments. When tested on whether participants can express awareness of the relationship between specific auditory stimulus and the distractor location, they showed explicit awareness only in Experiment 1. However, an exploratory analysis suggested a possibility of response biases at the awareness testing phase of Experiment 1. Overall, results indicate that irrespective of awareness of the relationship between auditory stimulus and distractor location regularities, there was no reliable influence of task-irrelevant auditory stimulus regularities on distractor suppression.

Pupil size as a robust marker of attentional bias toward nicotine-related stimuli in smokers

Spatial attention can be magnetically attracted by behaviorally salient stimuli. This phenomenon occasionally conflicts with behavioral goals, leading to maladaptive consequences, as in the case of addiction, in which attentional biases have been described and linked with clinically meaningful variables, such as craving level or dependence intensity. Here, we sought to probe the markers of attentional priority in smokers through eye-tracking measures, by leveraging the established link between eye movements and spatial attention. We were particularly interested in potential markers related to pupil size, because pupil diameter reflects a range of autonomic, affective, and cognitive/attentional reactions to behaviorally significant stimuli and is a robust marker of appetitive and aversive learning. We found that changes in pupil size to nicotine-related visual stimuli could reliably predict, in cross-validated logistic regression, the smoking status of young smokers (showing pupil constriction) better than more traditional proxy measures. The possibility that pupil constriction may reflect a bias toward central vision, for example, attentional capture, is discussed in terms of sensory tuning with respect to nicotine-related stimuli. Pupil size was more sensitive at lower nicotine dependence levels, and at increased abstinence time (though these two variables were collinear). We conclude that pupillometry can provide a robust marker for attentional priority computation and useful indications regarding motivational states and individual attitudes toward conditioned stimuli.

Habituation to abrupt-onset distractors with different spatial occurrence probability

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

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

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

Formant-invariant voice and pitch representations are pre-attentively formed from constantly varying speech and non-speech stimuli

The present study investigated whether listeners can form abstract voice representations while ignoring constantly changing phonological information and if they can use the resulting information to facilitate voice change detection. Further, the study aimed at understanding whether the use of abstraction is restricted to the speech domain or can be deployed also in non‐speech contexts. We ran an electroencephalogram (EEG) experiment including one passive and one active oddball task, each featuring a speech and a rotated speech condition. In the speech condition, participants heard constantly changing vowels uttered by a male speaker (standard stimuli) which were infrequently replaced by vowels uttered by a female speaker with higher pitch (deviant stimuli). In the rotated speech condition, participants heard rotated vowels, in which the natural formant structure of speech was disrupted. In the passive task, the mismatch negativity was elicited after the presentation of the deviant voice in both conditions, indicating that listeners could successfully group together different stimuli into a formant‐invariant voice representation. In the active task, participants showed shorter reaction times (RTs), higher accuracy and a larger P3b in the speech condition with respect to the rotated speech condition. Results showed that whereas at a pre‐attentive level the cognitive system can track pitch regularities while presumably ignoring constantly changing formant information both in speech and in rotated speech, at an attentive level the use of such information is facilitated for speech. This facilitation was also testified by a stronger synchronisation in the theta band (4–7 Hz), potentially pointing towards differences in encoding/retrieval processes.

Learning to suppress a location does not depend on knowing which location

The present study investigated whether explicit knowledge and awareness regarding the regularities present in the display affects statistical learning (SL) in visual search. Participants performed the additional singleton paradigm in which a salient distractor was presented much more often in one location than in all other locations. Previous studies have shown that participants learn this regularity as the location that is most likely to contain a distractor becomes suppressed relative to all other locations. In the current study, after each trial, participants had to either indicate the location of the distractor or the location of the target. Those participants that reported the distractor location, were very much aware of the regularity present in the display. However, participants that reported the target location were basically unaware of the regularity regarding the distractor. The results showed no difference between these groups in the amount of suppression of the high-probability location. This indicates that regardless of whether participants had explicit knowledge or not, the suppression was basically the same. We conclude that explicit knowledge and awareness does not contribute to learning to suppress a location. This conclusion is consistent with the notion that statistical learning is automatic, operating without conscious effort or awareness.

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

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

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.

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.

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.

Comparing the motivational value of rewards and losses in an EEG-pupillometry study

We found earlier that performance-contingent rewards lead to faster performance than equivalent losses [Carsten, Hoofs, Boehler, & Krebs, 2019. Motivation Science, 5(3). http://dx.doi.org/10.1037/mot0000117]. Here, we further tested the hypothesis that motivation to gain rewards is higher than to avoid losses, even when incentive values are matched. As implicit markers of motivation, we assessed electroencephalography (EEG) focusing on the P3 after target and feedback onset, and the Feedback-Related Negativity (FRN), as well as simultaneously recorded pupil size. Comparing only reward and loss prospect trials in Experiment 1, we found no consistent differences in behavior and electrophysiological markers of motivation, although pupil data suggested higher arousal after feedback in potential-loss trials. Including additional no-incentive trials in Experiment 2, we found consistent evidence that motivation to gain rewards was higher than to avoid losses: In line with behavior, the target-P3 was most pronounced for reward-related stimuli, followed by loss and no-incentive ones. This same ranking was found in the P3 and the FRN after positive outcomes (i.e., reward, avoided loss, and correct feedback in no-incentive trials). Negative outcomes featured a different pattern in line with the pupil response, which suggests that losses are emotionally salient events, without invigorating behavior proportionally. In sum, these findings suggest that the motivation to gain rewards is more pronounced than motivation to avoid equivalent losses, at least in tasks promoting transient increases in attention triggered by incentive prospect. These motivational differences may arise as avoided losses are not profitable in the long term, in contrast to gained rewards.