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The role of sensory uncertainty in simple contour integration

Perceptual organization is the process of grouping scene elements into whole entities. A classic example is contour integration, in which separate line segments are perceived as continuous contours. Uncertainty in such grouping arises from scene ambiguity and sensory noise. Some classic Gestalt prin...

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Detalles Bibliográficos
Autores principales: Zhou, Yanli, Acerbi, Luigi, Ma, Wei Ji
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7728286/
https://www.ncbi.nlm.nih.gov/pubmed/33253195
http://dx.doi.org/10.1371/journal.pcbi.1006308
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author Zhou, Yanli
Acerbi, Luigi
Ma, Wei Ji
author_facet Zhou, Yanli
Acerbi, Luigi
Ma, Wei Ji
author_sort Zhou, Yanli
collection PubMed
description Perceptual organization is the process of grouping scene elements into whole entities. A classic example is contour integration, in which separate line segments are perceived as continuous contours. Uncertainty in such grouping arises from scene ambiguity and sensory noise. Some classic Gestalt principles of contour integration, and more broadly, of perceptual organization, have been re-framed in terms of Bayesian inference, whereby the observer computes the probability that the whole entity is present. Previous studies that proposed a Bayesian interpretation of perceptual organization, however, have ignored sensory uncertainty, despite the fact that accounting for the current level of perceptual uncertainty is one of the main signatures of Bayesian decision making. Crucially, trial-by-trial manipulation of sensory uncertainty is a key test to whether humans perform near-optimal Bayesian inference in contour integration, as opposed to using some manifestly non-Bayesian heuristic. We distinguish between these hypotheses in a simplified form of contour integration, namely judging whether two line segments separated by an occluder are collinear. We manipulate sensory uncertainty by varying retinal eccentricity. A Bayes-optimal observer would take the level of sensory uncertainty into account—in a very specific way—in deciding whether a measured offset between the line segments is due to non-collinearity or to sensory noise. We find that people deviate slightly but systematically from Bayesian optimality, while still performing “probabilistic computation” in the sense that they take into account sensory uncertainty via a heuristic rule. Our work contributes to an understanding of the role of sensory uncertainty in higher-order perception.
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spelling pubmed-77282862020-12-17 The role of sensory uncertainty in simple contour integration Zhou, Yanli Acerbi, Luigi Ma, Wei Ji PLoS Comput Biol Research Article Perceptual organization is the process of grouping scene elements into whole entities. A classic example is contour integration, in which separate line segments are perceived as continuous contours. Uncertainty in such grouping arises from scene ambiguity and sensory noise. Some classic Gestalt principles of contour integration, and more broadly, of perceptual organization, have been re-framed in terms of Bayesian inference, whereby the observer computes the probability that the whole entity is present. Previous studies that proposed a Bayesian interpretation of perceptual organization, however, have ignored sensory uncertainty, despite the fact that accounting for the current level of perceptual uncertainty is one of the main signatures of Bayesian decision making. Crucially, trial-by-trial manipulation of sensory uncertainty is a key test to whether humans perform near-optimal Bayesian inference in contour integration, as opposed to using some manifestly non-Bayesian heuristic. We distinguish between these hypotheses in a simplified form of contour integration, namely judging whether two line segments separated by an occluder are collinear. We manipulate sensory uncertainty by varying retinal eccentricity. A Bayes-optimal observer would take the level of sensory uncertainty into account—in a very specific way—in deciding whether a measured offset between the line segments is due to non-collinearity or to sensory noise. We find that people deviate slightly but systematically from Bayesian optimality, while still performing “probabilistic computation” in the sense that they take into account sensory uncertainty via a heuristic rule. Our work contributes to an understanding of the role of sensory uncertainty in higher-order perception. Public Library of Science 2020-11-30 /pmc/articles/PMC7728286/ /pubmed/33253195 http://dx.doi.org/10.1371/journal.pcbi.1006308 Text en © 2020 Zhou et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Zhou, Yanli
Acerbi, Luigi
Ma, Wei Ji
The role of sensory uncertainty in simple contour integration
title The role of sensory uncertainty in simple contour integration
title_full The role of sensory uncertainty in simple contour integration
title_fullStr The role of sensory uncertainty in simple contour integration
title_full_unstemmed The role of sensory uncertainty in simple contour integration
title_short The role of sensory uncertainty in simple contour integration
title_sort role of sensory uncertainty in simple contour integration
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7728286/
https://www.ncbi.nlm.nih.gov/pubmed/33253195
http://dx.doi.org/10.1371/journal.pcbi.1006308
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