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

Attentional suppression of dynamic versus static salient distractors

Attention must be carefully controlled to avoid distraction by salient stimuli. The signal suppression hypothesis proposes that salient stimuli can be proactively suppressed to prevent distraction. Although this hypothesis has garnered much support, most previous studies have used one class of salient distractors: color singletons. It therefore remains unclear whether other kinds of salient distractors can also be suppressed. The current study directly compared suppression of a variety of salient stimuli using an attentional capture task that was adapted for eye tracking. The working hypothesis was that static salient stimuli (e.g., color singletons) would be easier to suppress than dynamic salient stimuli (e.g., motion singletons). The results showed that participants could ignore a wide variety of salient distractors. Importantly, suppression was weaker and slower to develop for dynamic salient stimuli than static salient stimuli. A final experiment revealed that adding a static salient feature to a dynamic motion distractor greatly improved suppression. Altogether, the results suggest that an underlying inhibitory process is applied to all kinds of salient distractors, but that suppression is more readily applied to static features than dynamic features.

Object-based suppression in target search but not in distractor inhibition

The present study investigated the effect of object representation on attentional priority regarding distractor inhibition and target search processes while the statistical regularities of singleton distractor location were biased. A color singleton distractor appeared more frequently at one of six stimulus locations, called the 'high-probability location,' to induce location-based suppression. Critically, three objects were presented, each of which paired two adjacent stimuli in a target display by adding background contours (Experiment 1) or using perceptual grouping (Experiments 2 and 3). The results revealed that attention capture by singleton distractors was hardly modulated by objects. In contrast, target selection was impeded at the location in the object containing the high-probability location compared to an equidistant location in a different object. This object-based suppression in target selection was evident when object-related features were parts of task-relevant features. These findings suggest that task-irrelevant objects modulate attentional suppression. Moreover, different features are engaged in determining attentional priority for distractor inhibition and target search processes.

No evidence for contextual cueing beyond explicit recognition

Many studies claim that visual regularities can be learned unconsciously and without explicit awareness. For example in the contextual cueing paradigm, studies often make claims using a standard reasoning based on two results: (1) a reliable response time (RT) difference between repeated vs. new stimulus displays and (2) a close-to-chance sensitivity when participants are asked to explicitly recognize repeated stimulus displays. From this pattern of results, studies routinely conclude that the sensitivity of RT responses is higher than that of explicit responses-an empirical situation we call Indirect Task Advantage (ITA). Many studies further infer from an ITA that RT effects were driven by a form of recognition that exceeds explicit memory: implicit recognition. However, this reasoning is flawed because the sensitivity underlying RT effects is never computed. To properly establish a difference, a sensitivity comparison is required. We apply this sensitivity comparison in a reanalysis of 20 contextual cueing studies showing that not a single study provides consistent evidence for ITAs. Responding to recent correlation-based arguments, we also demonstrate the absence of evidence for ITAs at the level of individual participants. This lack of ITAs has serious consequences for the field: If RT effects can be fully explained by weak but above-chance explicit recognition sensitivity, what is the empirical content of the label "implicit"? Thus, theoretical discussions in this paradigm-and likely in other paradigms using this standard reasoning-require serious reassessment because the current data from contextual cueing studies is insufficient to consider recognition as implicit.

Stability of individual differences in implicitly guided attention

Daily activities often occur in familiar environments, affording us an opportunity to learn. Laboratory studies have shown that people readily acquire an implicit spatial preference for locations that frequently contained a search target in the past. These studies, however, have focused on group characteristics, downplaying the significance of individual differences. In a pre-registered study, we examined the stability of individual differences in two variants of an implicit location probability learning (LPL) task. We tested the possibility that individual differences were stable in variants that shared the same search process, but not in variants involving different search processes. In Experiment 1, participants performed alternating blocks of T-among-Ls and 5-among-2s search tasks. Unbeknownst to them, the search target appeared disproportionately often in one region of space; the high-probability regions differed between the two tasks. LPL transferred between the two tasks. In addition, individuals who showed greater LPL in the T-task also did so in the 5-task and vice versa. In Experiment 2, participants searched for either a camouflaged-T against background noise or a well-segmented T among well-segmented Ls. These two tasks produced task-specific learning that did not transfer between tasks. Moreover, individual differences in learning did not correlate between tasks. Thus, LPL is associated with stable individual differences across variants, but only when the variants share common search processes.

Eye movements reflect active statistical learning

What is the link between eye movements and sensory learning? Although some theories have argued for an automatic interaction between what we know and where we look that continuously modulates human information gathering behavior during both implicit and explicit learning, there exists limited experimental evidence supporting such an ongoing interplay. To address this issue, we used a visual statistical learning paradigm combined with a gaze-contingent stimulus presentation and manipulated the explicitness of the task to explore how learning and eye movements interact. During both implicit exploration and explicit visual learning of unknown composite visual scenes, spatial eye movement patterns systematically and gradually changed in accordance with the underlying statistical structure of the scenes. Moreover, the degree of change was directly correlated with the amount and type of knowledge the observers acquired. This suggests that eye movements are potential indicators of active learning, a process where long-term knowledge, current visual stimuli and an inherent tendency to reduce uncertainty about the visual environment jointly determine where we look.

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.

Trichotomy revisited: A monolithic theory of attentional control

The control of attention was long held to reflect the influence of two competing mechanisms of assigning priority, one goal-directed and the other stimulus-driven. Learning-dependent influences on the control of attention that could not be attributed to either of those two established mechanisms of control gave rise to the concept of selection history and a corresponding third mechanism of attentional control. The trichotomy framework that ensued has come to dominate theories of attentional control over the past decade, replacing the historical dichotomy. In this theoretical review, I readily affirm that distinctions between the influence of goals, salience, and selection history are substantive and meaningful, and that abandoning the dichotomy between goal-directed and stimulus-driven mechanisms of control was appropriate. I do, however, question whether a theoretical trichotomy is the right answer to the problem posed by selection history. If we reframe the influence of goals and selection history as different flavors of memory-dependent modulations of attentional priority and if we characterize the influence of salience as a consequence of insufficient competition from such memory-dependent sources of priority, it is possible to account for a wide range of attention-related phenomena with only one mechanism of control. The monolithic framework for the control of attention that I propose offers several concrete advantages over a trichotomy framework, which I explore here.

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.

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.

Effector-independent Representations Guide Sequential Target Selection Biases in Action

Previous work shows that automatic attention biases toward recently selected target features transfer across action and perception and even across different effectors such as the eyes and hands on a trial-by-trial basis. Although these findings suggest a common neural representation of selection history across effectors, the extent to which information about recently selected target features is encoded in overlapping versus distinct brain regions is unknown. Using fMRI and a priming of pop-out task where participants selected unpredictable, uniquely colored targets among homogeneous distractors via reach or saccade, we show that color priming is driven by shared, effector-independent underlying representations of recent selection history. Consistent with previous work, we found that the intraparietal sulcus (IPS) was commonly activated on trials where target colors were switched relative to those where the colors were repeated; however, the dorsal anterior insula exhibited effector-specific activation related to color priming. Via multivoxel cross-classification analyses, we further demonstrate that fine-grained patterns of activity in both IPS and the medial temporal lobe encode information about selection history in an effector-independent manner, such that ROI-specific models trained on activity patterns during reach selection could predict whether a color was repeated or switched on the current trial during saccade selection and vice versa. Remarkably, model generalization performance in IPS and medial temporal lobe also tracked individual differences in behavioral priming sensitivity across both types of action. These results represent a first step to clarify the neural substrates of experience-driven selection biases in contexts that require the coordination of multiple actions.

Hebbian learning can explain rhythmic neural entrainment to statistical regularities

In many domains, learners extract recurring units from continuous sequences. For example, in unknown languages, fluent speech is perceived as a continuous signal. Learners need to extract the underlying words from this continuous signal and then memorize them. One prominent candidate mechanism is statistical learning, whereby learners track how predictive syllables (or other items) are of one another. Syllables within the same word predict each other better than syllables straddling word boundaries. But does statistical learning lead to memories of the underlying words-or just to pairwise associations among syllables? Electrophysiological results provide the strongest evidence for the memory view. Electrophysiological responses can be time-locked to statistical word boundaries (e.g., N400s) and show rhythmic activity with a periodicity of word durations. Here, I reproduce such results with a simple Hebbian network. When exposed to statistically structured syllable sequences (and when the underlying words are not excessively long), the network activation is rhythmic with the periodicity of a word duration and activation maxima on word-final syllables. This is because word-final syllables receive more excitation from earlier syllables with which they are associated than less predictable syllables that occur earlier in words. The network is also sensitive to information whose electrophysiological correlates were used to support the encoding of ordinal positions within words. Hebbian learning can thus explain rhythmic neural activity in statistical learning tasks without any memory representations of words. Learners might thus need to rely on cues beyond statistical associations to learn the words of their native language. RESEARCH HIGHLIGHTS: Statistical learning may be utilized to identify recurring units in continuous sequences (e.g., words in fluent speech) but may not generate explicit memory for words. Exposure to statistically structured sequences leads to rhythmic activity with a period of the duration of the underlying units (e.g., words). I show that a memory-less Hebbian network model can reproduce this rhythmic neural activity as well as putative encodings of ordinal positions observed in earlier research. Direct tests are needed to establish whether statistical learning leads to declarative memories for words.

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.

The Relation Between Attention and Memory

The relation between attention and memory has long been deemed important for understanding cognition, and it was heavily researched even in the first experimental psychology laboratory by Wilhelm Wundt and his colleagues. Since then, the importance of the relation between attention and memory has been explored in myriad subdisciplines of psychology, and we incorporate a wide range of these diverse fields. Here, we examine some of the practical consequences of this relation and summarize work with various methodologies relating attention to memory in the fields of working memory, long-term memory, individual differences, life-span development, typical brain function, and neuropsychological conditions. We point out strengths and unanswered questions for our own embedded processes view of information processing, which is used to organize a large body of evidence. Last, we briefly consider the relation of the evidence to a range of other theoretical views before drawing conclusions about the state of the field.

Attentional blur and blink: Effects of adaptive attentional scaling on visual awareness

Attentional scaling is a crucial mechanism that enables us to flexibly allocate our attention to larger or smaller regions in the visual field. Although previous studies have demonstrated the critical role of attentional scaling in visual processing, its impact on modulating visual awareness is not yet fully understood. This study investigates the adaptive control of attentional scaling and its influence on visual awareness in an attentional blink paradigm. Participants were required to attend to the first target's location, which was manipulated either session-wise, trial-wise, or such that it could be learned across a block of trials. Discrete, all-or-none, awareness was expected when attention was allocated to a narrow area, while gradual awareness was expected when attention was allocated to a larger area. We used mixture modeling to assess second target awareness across these different attentional scales. The results revealed that participants could adaptively control their attentional scale both across stable sessions, and through (implicit) statistical learning in blocks of successive trials. This produced gradual perceptual awareness when the participants adopted a broad attentional scale, causing an attentional "blur". However, trial-wise cues did not allow for attentional scaling, resulting in more discrete target perception overall, and an attentional "blink". We conclude that the attentional scale is to some extent under adaptive control during the attentional blink/blur, where it can produce qualitatively different modes of perceptual awareness.

Little engagement of attention by salient distractors defined in a different dimension or modality to the visual search target

Singleton distractors may inadvertently capture attention, interfering with the task at hand. The underlying neural mechanisms of how we prevent or handle distractor interference remain elusive. Here, we varied the type of salient distractor introduced in a visual search task: the distractor could be defined in the same (shape) dimension as the target, a different (color) dimension, or a different (tactile) modality (intra-dimensional, cross-dimensional, and, respectively, cross-modal distractor, all matched for physical salience); and besides behavioral interference, we measured lateralized electrophysiological indicators of attentional selectivity (the N2pc, Ppc, PD , CCN/CCP, CDA, and cCDA). The results revealed the intra-dimensional distractor to produce the strongest reaction-time interference, associated with the smallest target-elicited N2pc. In contrast, the cross-dimensional and cross-modal distractors did not engender any significant interference, and the target-elicited N2pc was comparable to the condition in which the search display contained only the target singleton, thus ruling out early attentional capture. Moreover, the cross-modal distractor elicited a significant early CCN/CCP, but did not influence the target-elicited N2pc, suggesting that the tactile distractor is registered by the somatosensory system (rather than being proactively suppressed), without, however, engaging attention. Together, our findings indicate that, in contrast to distractors defined in the same dimension as the target, distractors singled out in a different dimension or modality can be effectively prevented to engage attention, consistent with dimension- or modality-weighting accounts of attentional priority computation.

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.

The Distractor Positivity Component and the Inhibition of Distracting Stimuli

There has been a long-lasting debate about whether salient stimuli, such as uniquely colored objects, have the ability to automatically distract us. To resolve this debate, it has been suggested that salient stimuli do attract attention but that they can be suppressed to prevent distraction. Some research supporting this viewpoint has focused on a newly discovered ERP component called the distractor positivity (PD), which is thought to measure an inhibitory attentional process. This collaborative review summarizes previous research relying on this component with a specific emphasis on how the PD has been used to understand the ability to ignore distracting stimuli. In particular, we outline how the PD component has been used to gain theoretical insights about how search strategy and learning can influence distraction. We also review alternative accounts of the cognitive processes indexed by the PD component. Ultimately, we conclude that the PD component is a useful tool for understanding inhibitory processes related to distraction and may prove to be useful in other areas of study related to cognitive control.

How can we apply decision-making theories to wild animal behavior? Predictions arising from dual process theory and Bayesian decision theory

Our understanding of decision-making processes and cognitive biases is ever increasing, thanks to an accumulation of testable models and a large body of research over the last several decades. The vast majority of this work has been done in humans and laboratory animals because these study subjects and situations allow for tightly controlled experiments. However, it raises questions about how this knowledge can be applied to wild animals in their complex environments. Here, we review two prominent decision-making theories, dual process theory and Bayesian decision theory, to assess the similarities in these approaches and consider how they may apply to wild animals living in heterogenous environments within complicated social groupings. In particular, we wanted to assess when wild animals are likely to respond to a situation with a quick heuristic decision and when they are likely to spend more time and energy on the decision-making process. Based on the literature and evidence from our multi-destination routing experiments on primates, we find that individuals are likely to make quick, heuristic decisions when they encounter routine situations, or signals/cues that accurately predict a certain outcome, or easy problems that experience or evolutionary history has prepared them for. Conversely, effortful decision-making is likely in novel or surprising situations, when signals and cues have unpredictable or uncertain relationships to an outcome, and when problems are computationally complex. Though if problems are overly complex, satisficing via heuristics is likely, to avoid costly mental effort. We present hypotheses for how animals with different socio-ecologies may have to distribute their cognitive effort. Finally, we examine the conservation implications and potential cognitive overload for animals experiencing increasingly novel situations caused by current human-induced rapid environmental change.

Learning to suppress a location is configuration-dependent

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

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

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

Statistical learning of distractor locations is dependent on task context

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

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 of Distractor Suppression Downregulates Prestimulus Neural Excitability in Early Visual Cortex

Visual attention is highly influenced by past experiences. Recent behavioral research has shown that expectations about the spatial location of distractors within a search array are implicitly learned, with expected distractors becoming less interfering. Little is known about the neural mechanism supporting this form of statistical learning. Here, we used magnetoencephalography (MEG) to measure human brain activity to test whether proactive mechanisms are involved in the statistical learning of distractor locations. Specifically, we used a new technique called rapid invisible frequency tagging (RIFT) to assess neural excitability in early visual cortex during statistical learning of distractor suppression while concurrently investigating the modulation of posterior alpha band activity (8-12 Hz). Male and female human participants performed a visual search task in which a target was occasionally presented alongside a color-singleton distractor. Unbeknown to the participants, the distracting stimuli were presented with different probabilities across the two hemifields. RIFT analysis showed that early visual cortex exhibited reduced neural excitability in the prestimulus interval at retinotopic locations associated with higher distractor probabilities. In contrast, we did not find any evidence of expectation-driven distractor suppression in alpha band activity. These findings indicate that proactive mechanisms of attention are involved in predictive distractor suppression and that these mechanisms are associated with altered neural excitability in early visual cortex. Moreover, our findings indicate that RIFT and alpha band activity might subtend different and possibly independent attentional mechanisms.SIGNIFICANCE STATEMENT What we experienced in the past affects how we perceive the external world in the future. For example, an annoying flashing light might be better ignored if we know in advance where it usually appears. This ability of extracting regularities from the environment is called statistical learning. In this study, we explore the neuronal mechanisms allowing the attentional system to overlook items that are unequivocally distracting based on their spatial distribution. By recording brain activity using MEG while probing neural excitability with a novel technique called RIFT, we show that the neuronal excitability in early visual cortex is reduced in advance of stimulus presentation for locations where distracting items are more likely to occur.

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.

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.

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.

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.

A proposed attention-based model for spatial memory formation and retrieval

Animals use sensory information and memory to build internal representations of space. It has been shown that such representations extend beyond the geometry of an environment and also encode rich sensory experiences usually referred to as context. In mammals, contextual inputs from sensory cortices appear to be converging on the hippocampus as a key area for spatial representations and memory. How metric and external sensory inputs (e.g., visual context) are combined into a coherent and stable place representation is not fully understood. Here, I review the evidence of attentional effects along the ventral visual pathway and in the medial temporal lobe and propose an attention-based model for the integration of visual context in spatial representations. I further suggest that attention-based retrieval of spatial memories supports a feedback mechanism that allows consolidation of old memories and new sensory experiences related to the same place, thereby contributing to the stability of spatial representations. The resulting model has the potential to generate new hypotheses to explain complex responses of spatial cells such as place cells in the hippocampus.