Bayesian sampling in visual perception
Identifieur interne : 002C91 ( Main/Exploration ); précédent : 002C90; suivant : 002C92Bayesian sampling in visual perception
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.
Url:
DOI: 10.1073/pnas.1101430108
PubMed: 21742982
PubMed Central: 3145684
Affiliations:
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Knill</name><affiliation><nlm:aff wicri:cut=" and" id="aff2">Department of Brain and Cognitive Sciences</nlm:aff></affiliation><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></author><author><name sortKey="Pouget, Alexandre" sort="Pouget, Alexandre" uniqKey="Pouget A" first="Alexandre" last="Pouget">Alexandre Pouget</name><affiliation><nlm:aff wicri:cut=" and" id="aff2">Department of Brain and Cognitive Sciences</nlm:aff></affiliation></author></analytic><series><title level="j">Proceedings of the National Academy of Sciences of the United States of America</title><idno type="ISSN">0027-8424</idno><idno type="eISSN">1091-6490</idno><imprint><date when="2011">2011</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><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></div></front></TEI><affiliations><list><country><li>Espagne</li></country></list><tree><noCountry><name sortKey="Knill, David C" sort="Knill, David C" uniqKey="Knill D" first="David C." last="Knill">David C. Knill</name><name sortKey="Pouget, Alexandre" sort="Pouget, Alexandre" uniqKey="Pouget A" first="Alexandre" last="Pouget">Alexandre Pouget</name></noCountry><country name="Espagne"><noRegion><name sortKey="Moreno Bote, Ruben" sort="Moreno Bote, Ruben" uniqKey="Moreno Bote R" first="Rubén" last="Moreno-Bote">Rubén Moreno-Bote</name></noRegion></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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