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Salient but task-irrelevant distractors interfere less with visual search when they appear in a display region where distractors have appeared more frequently in the past (‘distractor-location probability cueing’). This effect could reflect the (re-)distribution of a global, limited attentional ‘inhibition resource’. Accordingly, changing the frequency of distractor appearance in one display region should also affect the magnitude of interference generated by distractors in a different region. Alternatively, distractor-location learning may reflect a local response (e.g., ‘habituation’) to distractors occurring at a particular location. In this case, the local distractor frequency in one display region should not affect distractor interference in a different region. To decide between these alternatives, we conducted three experiments in which participants searched for an orientation-defined target while ignoring a more salient orientation distractor that occurred more often in one vs. another display region. Experiment 1 varied the ratio of distractors appearing in the frequent vs. rare regions (60/40–90/10), with a fixed global distractor frequency. The results revealed the cueing effect to increase with increasing probability ratio. In Experiments 2 and 3, one (‘test’) region was assigned the same local distractor frequency as in one of the conditions of Experiment 1, but a different frequency in the other region – dissociating local from global distractor frequency. Together, the three experiments showed that distractor interference in the test region was not significantly influenced by the frequency in the other region, consistent with purely local learning. We discuss the implications for theories of statistical distractor-location learning.Public Significance StatementWe are frequently distracted by salient visual stimuli which are irrelevant to the task at hand. Previous studies have shown that ‘knowledge’ of the location(s) where a distractor is most likely to occur helps the observer to mitigate distraction. In this study we compared different theories of how the frequency and spatial distribution of distractor occurrence in different locations could influence the ability to avoid distraction. The results favored a local learning account: the ability to avoid distraction by distractors occuring in a particular spatial region is primarily influenced by how often distractors have occurred in that region.

Static statistical regularities in the placement of targets and salient distractors within the search display can be learned and used to optimize attentional guidance. Whether statistical learning also extends to dynamic regularities governing the placement of targets and distractors on successive trials has been less investigated. Here, we applied the same dynamic cross-trial regularity (one-step shift of the critical item in clock-/counterclockwise direction) either to the target or a distractor, and additionally varied whether the distractor was defined in a different (color) or the same dimension (shape) as the target. We found robust learning of the predicted target location: processing of the target at this (vs. a random) location was facilitated. But we found no evidence of proactive suppression of the predictable distractor location. Facilitation of the anticipated target location was associated with explicit awareness of the dynamic regularity, whereas participants showed no awareness of the distractor regularity. We propose that this asymmetry arises because, owing to the target’s central role in the task set, its location is explicitly encoded in working memory, enabling the learning of dynamic regularities. In contrast, the distractor is not explicitly encoded; so, statistical learning of distractor locations is limited to static regularities.Public significance statementCan we learn the cross-trial dynamic regularity of a target or a task-irrelevant salient distractor (e.g., one-step shift of the critical item in clock-/counterclockwise direction) to boost search performance? The present study found robust learning of the predicted target location, but no evidence of proactive suppression of the predictable distractor location. Facilitation of the anticipated target location was associated with explicit awareness of the dynamic regularity. This asymmetry highlights the important role of the target-centered task set in the learning of dynamic regularities.

Individuals with Autism Spectrum Disorder (ASD) are thought to under-rely on prior knowledge in perceptual decision-making. This study examined whether this applies to decisions of attention allocation, of relevance for ‘predictive-coding’ accounts of ASD. In a visual search task, a salient but task-irrelevant distractor appeared with higher probability in one display half. Individuals with ASD learned to avoid ‘attentional capture’ by distractors in the probable region as effectively as control participants—indicating typical priors for deploying attention. However, capture by a ‘surprising’ distractor at an unlikely location led to greatly slowed identification of a subsequent target at that location—indicating that individuals with ASD attempt to control surprise (unexpected attentional capture) by over-regulating parameters in post-selective decision-making. Supplementary Information The online version contains supplementary material available at 10.1007/s10803-020-04828-2.

Although time perception is based on the internal representation of time, whether the subjective timeline is scaled linearly or logarithmically remains an open issue. Evidence from previous research is mixed: while the classical internal-clock model assumes a linear scale with scalar variability, there is evidence that logarithmic timing provides a better fit to behavioral data. A major challenge for investigating the nature of the internal scale is that the retrieval process required for time judgments may involve a remapping of the subjective time back to the objective scale, complicating any direct interpretation of behavioral findings. Here, we used a novel approach, requiring rapid intuitive ‘ensemble’ averaging of a whole set of time intervals, to probe the subjective timeline. Specifically, observers’ task was to average a series of successively presented, auditory or visual, intervals in the time range 300-1300 ms. Importantly, the intervals were taken from three sets of durations, which were distributed such that the arithmetic mean (from the linear scale) and the geometric mean (from the logarithmic scale) were clearly distinguishable. Consistently across the three sets and the two presentation modalities, our results revealed subjective averaging to be close to the geometric mean, indicative of a logarithmic timeline underlying time perception.

Duration estimates are often biased by the sampled statistical context, yielding the classical central-tendency effect, i.e., short durations are over- and long duration underestimated. Most studies of the central-tendency bias have primarily focused on the integration of the sensory measure and the prior information, without considering any cognitive limits. Here, we investigated the impact of cognitive (visual working-memory) load on duration estimation in the duration encoding and reproduction stages. In four experiments, observers had to perform a dual, attention-sharing task: reproducing a given duration (primary) and memorizing a variable set of color patches (secondary). We found an increase in memory load (i.e., set size) during the duration-encoding stage to increase the central-tendency bias, while shortening the reproduced duration in general; in contrast, increasing the load during the reproduction stage prolonged the reproduced duration, without influencing the central tendency. By integrating an attentional-sharing account into a hierarchical Bayesian model, we were able to predict both the general over- and underestimation and the central-tendency effects observed in all four experiments. The model suggests that memory pressure during the encoding stage increases the sensory noise, which elevates the central-tendency effect. In contrast, memory pressure during the reproduction stage only influences the monitoring of elapsed time, leading to a general duration over-reproduction without impacting the central tendency.

Individuals with autism spectrum disorder (ASD) have been widely reported to show atypicalities in predictive coding, though there remains a controversy regarding what causes such atypical processing. Suggestions range from overestimation of volatility to rigidity in the reaction to environmental changes. Here, we tested two accounts directly using duration reproduction of volatile and non-volatile interval sequences, which were generated from the same set of intervals (i.e., the same ensemble prior). We found that both individuals with ASD and matched controls were able to adjust the weight of the ensemble prior for the reproduction according to the volatility of the sequence. However, the ASD group, as compared to the control group, relied generally less on the prior while also exhibiting marked carry-over of the weight of the prior when environmental volatility changes. Of note, though, four extremes among the 32 ASD individuals showed a reproduction pattern on the opposite end of the spectrum: heavy reliance on the prior in the volatile environment. Overall, our findings suggest that it is not the learning of the prior per se that is compromised in ASD. Rather, a less adaptive response to a change of the volatility regimen or to a volatile environment causes a highly inflexible weighting of prediction errors and the prior.

Observers can learn the locations where salient distractors appear frequently to reduce potential interference – an effect attributed to better suppression of distractors at frequent locations. But how distractor suppression is implemented in the visual cortex and frontoparietal attention networks remains unclear. We used fMRI and a regional distractor-location learning paradigm (Sauter et al. 2018, 2020) with two types of distractors defined in either the same (orientation) or a different (colour) dimension to the target to investigate this issue. fMRI results showed that BOLD signals in early visual cortex were significantly reduced for distractors (as well as targets) occurring at the frequent versus rare locations, mirroring behavioural patterns. This reduction was more robust with same-dimension distractors. Crucially, behavioural interference was correlated with distractor-evoked visual activity only for same- (but not different-) dimension distractors. Moreover, with different- (but not same-) dimension distractors, a colour-processing area within the fusiform gyrus was activated more when a colour distractor was present versus absent and with a distractor occurring at a rare versus frequent location. These results support statistical learning of frequent distractor locations involving regional suppression in the early visual cortex and point to differential neural mechanisms of distractor handling with different-versus same-dimension distractors.Competing Interest StatementThe authors have declared no competing interest.

Observers can learn the likely locations of salient distractors in visual search, reducing their potential to cause interference. While there is agreement that this involves suppression of frequent...

Searching for targets among similar distractors requires more time as the number of items increases, with search efficiency measured by the slope of the reaction-time (RT)/set-size function. Horowitz and Wolfe (Nature, 394(6693), 575-577, 1998) found that the target-present RT slopes were as similar for "dynamic" as for standard static search, even though the items were randomly reshuffled every 110 ms in dynamic search. Somewhat surprisingly, attempts to understand dynamic search have ignored that the target-absent RT slope was as low (or "flat") as the target-present slope-so that the mechanisms driving search performance under dynamic conditions remain unclear. Here, we report three experiments that further explored search in dynamic versus static displays. Experiment 1 confirmed that the target-absent:target-present slope ratio was close to or smaller than 1 in dynamic search, as compared with being close to or above 2 in static search. This pattern did not change when reward was assigned to either correct target-absent or correct target-present responses (Experiment 2), or when the search difficulty was increased (Experiment 3). Combining analysis of search sensitivity and response criteria, we developed a multiple-decisions model that successfully accounts for the differential slope patterns in dynamic versus static search. Two factors in the model turned out to be critical for generating the 1:1 slope ratio in dynamic search: the "quit-the-search" decision variable accumulated based upon the likelihood of "target absence" within each individual sample in the multiple-decisions process, whilst the stopping threshold was a linear function of the set size and reward manipulation.

In visual search tasks, repeating features or the position of the target results in faster response times. Such inter-trial ‘priming’ effects occur not just for repetitions from the immediately preceding trial but also from trials further back. A paradigm known to produce particularly long-lasting inter-trial effects–of the target-defining feature, target position, and response (feature)–is the ‘priming of pop-out’ (PoP) paradigm, which typically uses sparse search displays and random swapping across trials of target- and distractor-defining features. However, the mechanisms underlying these inter-trial effects are still not well understood. To address this, we applied a modeling framework combining an evidence accumulation (EA) model with different computational updating rules of the model parameters (i.e., the drift rate and starting point of EA) for different aspects of stimulus history, to data from a (previously published) PoP study that had revealed significant inter-trial effects from several trials back for repetitions of the target color, the target position, and (response-critical) target feature. By performing a systematic model comparison, we aimed to determine which EA model parameter and which updating rule for that parameter best accounts for each inter-trial effect and the associated n-back temporal profile. We found that, in general, our modeling framework could accurately predict the n-back temporal profiles. Further, target color- and position-based inter-trial effects were best understood as arising from redistribution of a limited-capacity weight resource which determines the EA rate. In contrast, response-based inter-trial effects were best explained by a bias of the starting point towards the response associated with a previous target; this bias appeared largely tied to the position of the target. These findings elucidate how our cognitive system continually tracks, and updates an internal predictive model of, a number of separable stimulus and response parameters in order to optimize task performance., Author summary In many perceptual tasks, performance is faster and more accurate when critical stimulus attributes are repeated from trial to trial compared to when they change. Priming of pop-out (PoP), visual search with sparse search displays and random swapping of the target feature between trials, is a paradigm in which such inter-trial effects can be traced back over several recent trial episodes. While many studies have explored PoP paradigms, the mechanisms underlying priming of the search-critical target feature, the target position, and the response-critical information are not yet fully understood. Here, we addressed this question by applying evidence accumulation (EA) decision models to the data from a previously published PoP study. The modeling framework combines evidence accumulation with Bayesian updating of the model parameters. Comparison of (> 1000) different combinations of decision models and updating rules revealed that the featural and positional priming effects were best explained by assuming that attentional “weight” resources are dynamically redistributed based on the recent history of target color and position, whereas response decisions are biased based on the recent history of the response-critical property of targets occuring at a particular (and nearby) position(s). These findings confirm that our cognitive system continually tracks, and updates an internal predictive model of, a number of separable stimulus and response parameters in order to optimize task performance.

Observers can learn the likely locations of salient distractors in visual search, reducing their potential to cause interference. Although there is agreement that this involves positional suppression of the likely distractor location(s), it is contentious at which stage the suppression operates: the search-guiding priority map, which integrates feature-contrast signals (e.g., generated by a red among green or a diamond among circular items) across dimensions, or the distractor-defining dimension. On the latter, dimension-based account (Sauter, Liesefeld, Zehetleitner, & Muller, 2018), processing of, say, a shape-defined target should be unaffected by distractor suppression when the distractor is defined by color, because in this case only color signals would be suppressed. At odds with this, Wang and Theeuwes (2018a) found slowed processing of the target when it appeared at the likely (vs. an unlikely) distractor location, consistent with priority-map-based suppression. Adopting their paradigm, the present study replicated this target location effect. Crucially, however, changing the paradigm by making the target appear as likely at the frequent as at any of the rare distractor locations and making the distractor/nondistractor color assignment consistent abolished the target location effect, without impacting the reduced interference for distractors at the frequent location. These findings support a flexible locus of spatial distractor suppression-priority-map- or dimension-based-depending on the prominence of distractor cues provided by the paradigm. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Observers can learn the likely locations of salient distractors in visual search, reducing their potential to capture attention (Ferrante et al., 2018; Sauter et al., 2018a; Wang & Theeuwes, 2018a). While there is agreement that this involves positional suppression of the likely distractor location(s), it is contentious at which stage of search guidance the suppression operates: the supra-dimensional priority map or feature-contrast signals within the distractor dimension. On the latter account, advocated by Sauter et al., target processing should be unaffected by distractor suppression when the target is defined in a different (non-suppressed) dimension to the target. At odds with this, Wang and Theeuwes found strong suppression not only of the (color) distractor, but also of the (shape) target when it appeared at the likely distractor location. Adopting their paradigm, the present study ruled out that increased cross-trial inhibition of the single frequent (frequently inhibited) as compared to any of the rare (rarely inhibited) distractor locations is responsible for this target-location effect. However, a reduced likelihood of the target appearing at the frequent vs. a rare distractor location contributes to this effect: removing this negative bias abolished the cost to target processing with increasing practice, indicative of a transition from priority-map‐ to dimension-based – and thus a flexible locus of – distractor suppression.Public Significance StatementDistraction by a salient visual stimulus outside the ‘focus’ of the task at hand occurs frequently. The present study examined whether and how ‘knowledge’ of the likely location(s) where the distractors occur helps the observer to mitigate distraction. The results confirmed that observers can learn to suppress distracting stimuli at likely locations. Further, they showed that, the suppression may occur at different levels in the hierarchically organized visual system where the priorities of which objects to be attended in the environment are determined.

The coefficient of variation (CV), also known as relative standard deviation, has been used to measure the constancy of the Weber fraction, a key signature of efficient neural coding in time perception. It has long been debated whether or not duration judgments follow Weber's law, with arguments based on examinations of the CV. However, what has been largely ignored in this debate is that the observed CVs may be modulated by temporal context and decision uncertainty, thus questioning conclusions based on this measure. Here, we used a temporal reproduction paradigm to examine the variation of the CV with two types of temporal context: full-range mixed vs. sub-range blocked intervals, separately for intervals presented in the visual and auditory modalities. We found a strong contextual modulation of both interval-duration reproductions and the observed CVs. We then applied a two-stage Bayesian model to predict those variations. Without assuming a violation of the constancy of the Weber fraction, our model successfully predicted the central-tendency effect and the variation in the CV. Our findings and modeling results indicate that both the accuracy and precision of our timing behavior are highly dependent on the temporal context and decision uncertainty. And, critically, they advise caution with using variations of the CV to reject the constancy of the Weber fraction of duration estimation.

Many previous studies on visual search have reported inter-trial effects, that is, observers respond faster when some target property, such as a defining feature or dimension, or the response associated with the target repeats versus changes across consecutive trial episodes. However, what processes drive these inter-trial effects is still controversial. Here, we investigated this question using a combination of Bayesian modeling of belief updating and evidence accumulation modeling in perceptual decision-making. In three visual singleton (‘pop-out’) search experiments, we explored how the probability of the response-critical states of the search display (e.g., target presence/absence) and the repetition/switch of the target-defining dimension (color/ orientation) affect reaction time distributions. The results replicated the mean reaction time (RT) inter-trial and dimension repetition/switch effects that have been reported in previous studies. Going beyond this, to uncover the underlying mechanisms, we used the Drift-Diffusion Model (DDM) and the Linear Approach to Threshold with Ergodic Rate (LATER) model to explain the RT distributions in terms of decision bias (starting point) and information processing speed (evidence accumulation rate). We further investigated how these different aspects of the decision-making process are affected by different properties of stimulus history, giving rise to dissociable inter-trial effects. We approached this question by (i) combining each perceptual decision making model (DDM or LATER) with different updating models, each specifying a plausible rule for updating of either the starting point or the rate, based on stimulus history, and (ii) comparing every possible combination of trial-wise updating mechanism and perceptual decision model in a factorial model comparison. Consistently across experiments, we found that the (recent) history of the response-critical property influences the initial decision bias, while repetition/switch of the target-defining dimension affects the accumulation rate, likely reflecting an implicit ‘top-down’ modulation process. This provides strong evidence of a disassociation between response- and dimension-based inter-trial effects., Author summary When a perceptual task is performed repeatedly, performance becomes faster and more accurate when there is little or no change of critical stimulus attributes across consecutive trials. This phenomenon has been explored in previous studies on visual ‘pop-out’ search, showing that participants can find and respond to a unique target object among distractors faster when properties of the target are repeated across trials. However, the processes that underlie these inter-trial effects are still not clearly understood. Here, we approached this question by performing three visual search experiments and applying mathematical modeling to the data. We combined models of perceptual decision making with Bayesian updating rules for the parameters of the decision making models, to capture the processing of visual information on each individual trial as well as possible mechanisms through which an influence can be carried forward from previous trials. A systematic comparison of how well different combinations of models explain the data revealed the best model to assume that perceptual decisions are biased based on the response-critical stimulus property on recent trials, while repetition of the visual dimension in which the target differs from the distractors (e.g., color or orientation) increases the speed of stimulus processing.

International audience; Humans can learn associations between stimuli and responses which allow for faster, more efficient behavior when the same response is required to the same stimulus in the future. This is called stimulus-response (S-R) priming. Perceptual representations are known to be modular and hierarchical, i.e. different brain areas represent different perceptual features and higher brain areas represent increasingly abstract properties of the stimulus. In this study we investigated how perceptually specific the stimulus in S-R priming is. In particular we wanted to test whether basic visual features play a role in the S-R associations. We used a novel stimulus: images of objects built from basic visual features. Participants performed a classification task on the objects. We found no significant effect on reaction times of switching vs. repeating perceptual features between presentations of the same object. This suggests that S-R associations involve a perceptually non-specific stimulus representation.

Neurons in cortical area MT respond well to transparent streaming motion in distinct depth planes, such as caused by observer self-motion, but do not contain subregions excited by opposite directions of motion. We therefore predicted that spatial resolution for transparent motion/disparity conjunctions would be limited by the size of MT receptive fields, just as spatial resolution for disparity is limited by the much smaller receptive fields found in primary visual cortex, V1. We measured this using a novel “joint motion/disparity grating,” on which human observers detected motion/disparity conjunctions in transparent random-dot patterns containing dots streaming in opposite directions on two depth planes. Surprisingly, observers showed the same spatial resolution for these as for pure disparity gratings. We estimate the limiting receptive field diameter at 11 arcmin, similar to V1 and much smaller than MT. Higher internal noise for detecting joint motion/disparity produces a slightly lower high-frequency cutoff of 2.5 cycles per degree (cpd) versus 3.3 cpd for disparity. This suggests that information on motion/disparity conjunctions is available in the population activity of V1 and that this information can be decoded for perception even when it is invisible to neurons in MT.

Stereo vision is an area in which we are increasingly able to construct detailed numerical models of the computations carried out by cerebral cortex. Piecewise-frontoparallel cross-correlation is one such model, closely based on the known physiology and able to explain important aspects of human stereo depth perception. Here, we show that it predicts important differences in the ability to detect disparity gratings with square-wave vs. sine-wave profiles. In particular, the model can detect square-wave gratings up to much higher disparity amplitudes than sine-wave gratings. We test this prediction in human subjects and find that it is not borne out. Rather there seems to be little or no difference between the detectability of square- and sine-wave disparity gratings for human subjects. We conclude that the model needs further refinement in order to capture this aspect of human stereo vision.