Representing Color Ensembles

Chetverikov, Andrey; Campana, Gianluca; Kristjánsson, Árni

doi:10.1177/0956797617713787

Cited by 80 publications

(123 citation statements)

References 28 publications

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“…These results corroborate previous findings of implicit encoding of distractor distributions (Chetverikov et al, 2016;2017a, 2017b2017c).…”

Section: Resultssupporting

confidence: 92%

“…This was done by fitting different models to our data with maximum likelihood estimation and comparing the models with the Bayesian Information Criterion (BIC). The models were selected with methods used in previous studies where observer's CT-PD functions differed between uniform and Gaussian distributions with the same range (Chetverikov et al 2017b(Chetverikov et al , 2017c(Chetverikov et al , 2019Hansmann-Roth et al 2019). Specifically, following a uniform distribution, observers' CT-PD functions have a flat segment with low CT-PD (high similarity between target and previous distractor distribution).…”

Section: Discussionmentioning

confidence: 99%

“…All experiments were performed in accordance with the requirements of the local ethics committee and the Declaration of Helsinki. Sample size and trial number were based on the results of previous studies (Chetverikov et al 2016, 2017a, 2017b, 2017c. In addition, we used model comparison and Bayes Factor analysis to estimate the amount of evidence in favor of our hypotheses rather than a traditional null-hypothesis frequentist testing.…”

Section: Observersmentioning

confidence: 99%

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Dissociating implicit and explicit ensemble representations reveals the limits of visual perception and the richness of behavior

Hansmann-Roth¹,

Kristjánsson²,

Whitney³

et al. 2020

Preprint

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Visual ensembles, like leaves on a tree, often share properties such as shape or color. Thisredundancy can be quantified as a feature probability distribution whose summary statistics (e.g.,mean) observers can report explicitly. Here, we show that such explicit reports underestimate therichness of ensemble perception. Participants (N=12 per condition) searched for an odd-one-outtarget among heterogeneous distractors and their memory of distractor characteristics was testedexplicitly or implicitly. Our observers could explicitly distinguish distractor sets with differentmean and variance, but not differently-shaped probability distributions. In contrast, implicitassessment revealed encoding of mean, variance, and importantly, also distribution shape.Furthermore, explicit measures had common noise sources that distinguished them from implicitmeasures. We conclude that feature distributions are encoded in rich detail and can guidebehaviour, while explicit summary judgments are based on information equivalent to a fewsamples from fully encoded distributions.

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“…These results corroborate previous findings of implicit encoding of distractor distributions (Chetverikov et al, 2016;2017a, 2017b2017c).…”

Section: Resultssupporting

confidence: 92%

Section: Discussionmentioning

confidence: 99%

Section: Observersmentioning

confidence: 99%

See 1 more Smart Citation

Dissociating implicit and explicit ensemble representations reveals the limits of visual perception and the richness of behavior

Hansmann-Roth¹,

Kristjánsson²,

Whitney³

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Crucially, experiments with varied set size and trial numbers show that learning in this paradigm cannot be explained by the sampling of a few items (Chetverikov, Campana, & Kristjánsson, 2017d, 2017b. It also cannot be explained by simple decision rule learning (e.g., all stimuli that have features in a certain range are distractors), because observers response times, on average, reflect the shape of the distractor distribution rather than just a boundary between a target and distractors (Chetverikov et al, 2016(Chetverikov et al, , 2017bChetverikov, Campana, & Kristjánsson, 2017c;Chetverikov, Hansmann-Roth, Tanrikulu, & Kristjansson, 2019). However, it is not yet clear whether each single set of learning trials can feed observers' templates with the feature probability distribution of distractors, nor is it clear how accurately the information is stored in the templates.…”

Section: Probabilistic Rejection Templates In Visual Working Memorymentioning

confidence: 99%

“…. Following our previous studies (Chetverikov et al, 2016(Chetverikov et al, , 2017b(Chetverikov et al, , 2017c(Chetverikov et al, , 2017d, the design of this study utilized within-subject comparisons with a relatively small number of trained observers (each observer was trained for at least 100 trials before the main session) performing a large number of trials. The sample size and the trial numbers were similar to those in previous studies using the same paradigm.…”

Section: Probabilistic Rejection Templates In Visual Working Memorymentioning

confidence: 99%

Probabilistic rejection templates in visual working memory

2020

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Our interactions with the visual world are guided by attention and visual working memory. Things that we look for and those we ignore are stored as templates that reflect our goals and the tasks at hand. The nature of such templates has been widely debated. A recent proposal is that these templates can be thought of as probabilistic representations of task-relevant features. Crucially, such probabilistic templates should accurately reflect feature probabilities in the environment. Here we ask whether observers can quickly form a correct internal model of a complex (bimodal) distribution of distractor features. We assessed observers' representations by measuring the slowing of visual search when target features unexpectedly match a distractor template. Distractor stimuli were heterogeneous, randomly drawn on each trial from a bimodal probability distribution. Using two targets on each trial, we tested whether observers encode the full distribution, only one peak of it, or the average of the two peaks. Search was slower when the two targets corresponded to the two modes of a previous distractor distribution than when one target was at one of the modes and another between them or outside the distribution range. Furthermore, targets on the modes were reported later than targets between the modes that, in turn, were reported later than targets outside this range. This shows that observers use a correct internal model, representing both distribution modes using templates based on the full probability distribution rather than just one peak or simple summary statistics. The findings further confirm that performance in odd-one out search with repeated distractors cannot be described by a simple decision rule. Our findings indicate that probabilistic visual working memory templates guiding attention, dynamically adapt to task requirements, accurately reflecting the probabilistic nature of the input.

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Structural and contextual priors affect visual search in children with and without autism

et al. 2021

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Bayesian predictive coding theories of autism spectrum disorder propose that impaired acquisition or a broader shape of prior probability distributions lies at the core of the condition. However, we still know very little about how probability distributions are learned and encoded by children, let alone children with autism.Here, we take advantage of a recently developed distribution learning paradigm to characterize how children with and without autism acquire information about probability distributions. Twenty-four autistic and 25-matched neurotypical children searched for an odd-one-out target among a set of distractor lines with orientations sampled from a Gaussian distribution repeated across multiple trials to allow for learning of the parameters (mean and variance) of the distribution. We could measure the width (variance) of the participant's encoded distribution by introducing a target-distractor role-reversal while varying the similarity between target and previous distractor mean. Both groups performed similarly on the visual search task and learned the distractor distribution to a similar extent. However, the variance learned was much broader than the one presented, consistent with less informative priors in children irrespective of autism diagnosis. These findings have important implications for Bayesian accounts of perception throughout development, and Bayesian accounts of autism specifically.Lay summary: Recent theories about the underlying cognitive mechanisms of autism propose that the way autistic individuals estimate variability or uncertainty in their perceptual environment may differ from how typical individuals do so. Children had to search an oddly tilted line in a set of lines pointing in different directions, and based on their response times we examined how they learned about the variability in a set of objects. We found that autistic children learn variability as well as typical children, but both groups learn with less precision than typical adults do on the same task.

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Representing Color Ensembles

Cited by 80 publications

References 28 publications

Dissociating implicit and explicit ensemble representations reveals the limits of visual perception and the richness of behavior

Dissociating implicit and explicit ensemble representations reveals the limits of visual perception and the richness of behavior

Probabilistic rejection templates in visual working memory

Structural and contextual priors affect visual search in children with and without autism

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