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Learning what to expect (in visual perception)

Expectations are known to greatly affect our experience of the world. A growing theory in computational neuroscience is that perception can be successfully described using Bayesian inference models and that the brain is “Bayes-optimal” under some constraints. In this context, expectations are partic...

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Detalles Bibliográficos
Autores principales: Seriès, Peggy, Seitz, Aaron R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3807544/
https://www.ncbi.nlm.nih.gov/pubmed/24187536
http://dx.doi.org/10.3389/fnhum.2013.00668
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author Seriès, Peggy
Seitz, Aaron R.
author_facet Seriès, Peggy
Seitz, Aaron R.
author_sort Seriès, Peggy
collection PubMed
description Expectations are known to greatly affect our experience of the world. A growing theory in computational neuroscience is that perception can be successfully described using Bayesian inference models and that the brain is “Bayes-optimal” under some constraints. In this context, expectations are particularly interesting, because they can be viewed as prior beliefs in the statistical inference process. A number of questions remain unsolved, however, for example: How fast do priors change over time? Are there limits in the complexity of the priors that can be learned? How do an individual’s priors compare to the true scene statistics? Can we unlearn priors that are thought to correspond to natural scene statistics? Where and what are the neural substrate of priors? Focusing on the perception of visual motion, we here review recent studies from our laboratories and others addressing these issues. We discuss how these data on motion perception fit within the broader literature on perceptual Bayesian priors, perceptual expectations, and statistical and perceptual learning and review the possible neural basis of priors.
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spelling pubmed-38075442013-11-01 Learning what to expect (in visual perception) Seriès, Peggy Seitz, Aaron R. Front Hum Neurosci Neuroscience Expectations are known to greatly affect our experience of the world. A growing theory in computational neuroscience is that perception can be successfully described using Bayesian inference models and that the brain is “Bayes-optimal” under some constraints. In this context, expectations are particularly interesting, because they can be viewed as prior beliefs in the statistical inference process. A number of questions remain unsolved, however, for example: How fast do priors change over time? Are there limits in the complexity of the priors that can be learned? How do an individual’s priors compare to the true scene statistics? Can we unlearn priors that are thought to correspond to natural scene statistics? Where and what are the neural substrate of priors? Focusing on the perception of visual motion, we here review recent studies from our laboratories and others addressing these issues. We discuss how these data on motion perception fit within the broader literature on perceptual Bayesian priors, perceptual expectations, and statistical and perceptual learning and review the possible neural basis of priors. Frontiers Media S.A. 2013-10-24 /pmc/articles/PMC3807544/ /pubmed/24187536 http://dx.doi.org/10.3389/fnhum.2013.00668 Text en Copyright © Seriès and Seitz. http://creativecommons.org/licenses/by/3.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Seriès, Peggy
Seitz, Aaron R.
Learning what to expect (in visual perception)
title Learning what to expect (in visual perception)
title_full Learning what to expect (in visual perception)
title_fullStr Learning what to expect (in visual perception)
title_full_unstemmed Learning what to expect (in visual perception)
title_short Learning what to expect (in visual perception)
title_sort learning what to expect (in visual perception)
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3807544/
https://www.ncbi.nlm.nih.gov/pubmed/24187536
http://dx.doi.org/10.3389/fnhum.2013.00668
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