Bayesian sampling in visual perception
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Auteurs : Rubén Moreno-Bote [Espagne] ; David C. Knill ; Alexandre PougetSource :
- Proceedings of the National Academy of Sciences of the United States of America [ 0027-8424 ] ; 2011.
Abstract
It is well-established that some aspects of perception and action can be understood as probabilistic inferences over underlying probability distributions. In some situations, it would be advantageous for the nervous system to sample interpretations from a probability distribution rather than commit to a particular interpretation. In this study, we asked whether visual percepts correspond to samples from the probability distribution over image interpretations, a form of sampling that we refer to as Bayesian sampling. To test this idea, we manipulated pairs of sensory cues in a bistable display consisting of two superimposed moving drifting gratings, and we asked subjects to report their perceived changes in depth ordering. We report that the fractions of dominance of each percept follow the multiplicative rule predicted by Bayesian sampling. Furthermore, we show that attractor neural networks can sample probability distributions if input currents add linearly and encode probability distributions with probabilistic population codes.
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DOI: 10.1073/pnas.1101430108
PubMed: 21742982
PubMed Central: 3145684
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David C. Knill<affiliation><nlm:aff id="aff3">Center for Visual Science,<institution>University of Rochester</institution>, Rochester, NY, 14627</nlm:aff><wicri:noCountry code="subfield">14627</wicri:noCountry></affiliation>Le document en format XML
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<front><journal-meta><journal-id journal-id-type="nlm-ta">Proc Natl Acad Sci U S A</journal-id><journal-id journal-id-type="hwp">pnas</journal-id><journal-id journal-id-type="pmc">pnas</journal-id><journal-id journal-id-type="publisher-id">PNAS</journal-id><journal-title-group><journal-title>Proceedings of the National Academy of Sciences of the United States of America</journal-title></journal-title-group><issn pub-type="ppub">0027-8424</issn><issn pub-type="epub">1091-6490</issn><publisher><publisher-name>National Academy of Sciences</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">21742982</article-id><article-id pub-id-type="pmc">3145684</article-id><article-id pub-id-type="publisher-id">201101430</article-id><article-id pub-id-type="doi">10.1073/pnas.1101430108</article-id><article-categories><subj-group subj-group-type="heading"><subject>Biological Sciences</subject><subj-group><subject>Neuroscience</subject></subj-group></subj-group><subj-group subj-group-type="heading"><subject>Social Sciences</subject><subj-group><subject>Psychological and Cognitive Sciences</subject></subj-group></subj-group></article-categories><title-group><article-title>Bayesian sampling in visual perception</article-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Moreno-Bote</surname><given-names>Rubén</given-names></name><xref ref-type="aff" rid="aff1"><sup>a</sup></xref><xref ref-type="aff" rid="aff2"><sup>b</sup></xref><xref ref-type="corresp" rid="cor1"><sup>1</sup></xref></contrib><contrib contrib-type="author"><name><surname>Knill</surname><given-names>David C.</given-names></name><xref ref-type="aff" rid="aff2"><sup>b</sup></xref><xref ref-type="aff" rid="aff3"><sup>c</sup></xref></contrib><contrib contrib-type="author"><name><surname>Pouget</surname><given-names>Alexandre</given-names></name><xref ref-type="aff" rid="aff2"><sup>b</sup></xref></contrib><aff id="aff1"><sup>a</sup><institution>Foundation Sant Joan de Déu</institution>, Parc Sanitari Sant Joan de Déu, Esplugues de Llobregat, 08950 Barcelona,<country>Spain</country>; and</aff><aff id="aff2"><sup>b</sup>Department of Brain and Cognitive Sciences and</aff><aff id="aff3"><sup>c</sup>Center for Visual Science,<institution>University of Rochester</institution>, Rochester, NY, 14627</aff></contrib-group><author-notes><corresp id="cor1"><sup>1</sup>To whom correspondence should be addressed. E-mail: <email>rmoreno@bcs.rochester.edu</email>.</corresp><fn fn-type="edited-by"><p>Edited by Wilson S. Geisler, University of Texas at Austin, Austin, TX, and approved May 31, 2011 (received for review January 27, 2011)</p></fn><fn fn-type="con"><p>Author contributions: R.M.-B., D.C.K., and A.P. designed research; R.M.-B., D.C.K., and A.P. performed research; R.M.-B. analyzed data; and R.M.-B., D.C.K., and A.P. wrote the paper.</p></fn></author-notes><pub-date pub-type="ppub"><day>26</day><month>7</month><year>2011</year></pub-date><pub-date pub-type="epub"><day>8</day><month>7</month><year>2011</year></pub-date><volume>108</volume><issue>30</issue><fpage>12491</fpage><lpage>12496</lpage><self-uri xlink:title="pdf" xlink:type="simple" xlink:href="pnas.201101430.pdf"></self-uri><abstract><p>It is well-established that some aspects of perception and action can be understood as probabilistic inferences over underlying probability distributions. In some situations, it would be advantageous for the nervous system to sample interpretations from a probability distribution rather than commit to a particular interpretation. In this study, we asked whether visual percepts correspond to samples from the probability distribution over image interpretations, a form of sampling that we refer to as Bayesian sampling. To test this idea, we manipulated pairs of sensory cues in a bistable display consisting of two superimposed moving drifting gratings, and we asked subjects to report their perceived changes in depth ordering. We report that the fractions of dominance of each percept follow the multiplicative rule predicted by Bayesian sampling. Furthermore, we show that attractor neural networks can sample probability distributions if input currents add linearly and encode probability distributions with probabilistic population codes.</p></abstract><kwd-group><kwd>Bayesian inference</kwd><kwd>neuronal network</kwd><kwd>neuronal noise</kwd><kwd>perceptual bistability</kwd></kwd-group></article-meta></front></pmc></record>Pour manipuler ce document sous Unix (Dilib)
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