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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.

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...

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 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.

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.

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.