Learning what to expect (in visual perception)
Identifieur interne : 001A73 ( Main/Merge ); précédent : 001A72; suivant : 001A74Learning what to expect (in visual perception)
Auteurs : Peggy Seriès [Royaume-Uni] ; Aaron R. Seitz [États-Unis]Source :
- Frontiers in Human Neuroscience [ 1662-5161 ] ; 2013.
Abstract
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.
Url:
DOI: 10.3389/fnhum.2013.00668
PubMed: 24187536
PubMed Central: 3807544
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Seitz</name><affiliation wicri:level="1"><nlm:aff id="aff2"><institution>Department of Psychology, University of California at Riverside</institution><country>Riverside, CA, USA</country></nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea></wicri:regionArea><wicri:regionArea># see nlm:aff region in country</wicri:regionArea></affiliation></author></analytic><series><title level="j">Frontiers in Human Neuroscience</title><idno type="eISSN">1662-5161</idno><imprint><date when="2013">2013</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>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.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Acerbi, L" uniqKey="Acerbi L">L. Acerbi</name></author><author><name sortKey="Wolpert, D M" uniqKey="Wolpert D">D. M. Wolpert</name></author><author><name sortKey="Vijayakumar, S" uniqKey="Vijayakumar S">S. Vijayakumar</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Adab, H Z" uniqKey="Adab H">H. Z. Adab</name></author><author><name sortKey="Vogels, R" uniqKey="Vogels R">R. Vogels</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Adams, W J" uniqKey="Adams W">W. J. Adams</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Adams, W J" uniqKey="Adams W">W. J. 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Klein</name></author><author><name sortKey="Levi, D M" uniqKey="Levi D">D. M. Levi</name></author><author><name sortKey="Yu, C" uniqKey="Yu C">C. Yu</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Zhang, R" uniqKey="Zhang R">R. Zhang</name></author><author><name sortKey="Kwon, O S" uniqKey="Kwon O">O. S. Kwon</name></author><author><name sortKey="Tadin, D" uniqKey="Tadin D">D. Tadin</name></author></analytic></biblStruct></listBibl></div1></back></TEI></record>Pour manipuler ce document sous Unix (Dilib)
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