How much to trust the senses: Likelihood learning
Identifieur interne : 001144 ( Main/Exploration ); précédent : 001143; suivant : 001145How much to trust the senses: Likelihood learning
Auteurs : Yoshiyuki Sato [Japon] ; Konrad P. Kording [États-Unis]Source :
- Journal of Vision [ 1534-7362 ] ; 2014.
Abstract
Our brain often needs to estimate unknown variables from imperfect information. Our knowledge about the statistical distributions of quantities in our environment (called priors) and currently available information from sensory inputs (called likelihood) are the basis of all Bayesian models of perception and action. While we know that priors are learned, most studies of prior-likelihood integration simply assume that subjects know about the likelihood. However, as the quality of sensory inputs change over time, we also need to learn about new likelihoods. Here, we show that human subjects readily learn the distribution of visual cues (likelihood function) in a way that can be predicted by models of statistically optimal learning. Using a likelihood that depended on color context, we found that a learned likelihood generalized to new priors. Thus, we conclude that subjects learn about likelihood.
Url:
DOI: 10.1167/14.13.13
PubMed: 25398975
PubMed Central: 4233767
Affiliations:
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Kording</name><affiliation wicri:level="2"><nlm:aff id="aff2">Departments of Physical Medicine and Rehabilitation, Physiology, and Applied Mathematics, Northwestern University, and Rehabilitation Institute of Chicago, IL, USA</nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Departments of Physical Medicine and Rehabilitation, Physiology, and Applied Mathematics, Northwestern University, and Rehabilitation Institute of Chicago, IL</wicri:regionArea><placeName><region type="state">Illinois</region></placeName></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">25398975</idno><idno type="pmc">4233767</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4233767</idno><idno type="RBID">PMC:4233767</idno><idno type="doi">10.1167/14.13.13</idno><date when="2014">2014</date><idno type="wicri:Area/Pmc/Corpus">001493</idno><idno type="wicri:Area/Pmc/Curation">001493</idno><idno type="wicri:Area/Pmc/Checkpoint">000B99</idno><idno type="wicri:Area/Ncbi/Merge">003451</idno><idno type="wicri:Area/Ncbi/Curation">003451</idno><idno type="wicri:Area/Ncbi/Checkpoint">003451</idno><idno type="wicri:Area/Main/Merge">001146</idno><idno type="wicri:Area/Main/Curation">001144</idno><idno type="wicri:Area/Main/Exploration">001144</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">How much to trust the senses: Likelihood learning</title><author><name sortKey="Sato, Yoshiyuki" sort="Sato, Yoshiyuki" uniqKey="Sato Y" first="Yoshiyuki" last="Sato">Yoshiyuki Sato</name><affiliation wicri:level="1"><nlm:aff id="aff1">Graduate School of Information Systems, University of Electro-Communications, Japan</nlm:aff><country xml:lang="fr">Japon</country><wicri:regionArea>Graduate School of Information Systems, University of Electro-Communications</wicri:regionArea><wicri:noRegion>University of Electro-Communications</wicri:noRegion></affiliation></author><author><name sortKey="Kording, Konrad P" sort="Kording, Konrad P" uniqKey="Kording K" first="Konrad P." last="Kording">Konrad P. Kording</name><affiliation wicri:level="2"><nlm:aff id="aff2">Departments of Physical Medicine and Rehabilitation, Physiology, and Applied Mathematics, Northwestern University, and Rehabilitation Institute of Chicago, IL, USA</nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Departments of Physical Medicine and Rehabilitation, Physiology, and Applied Mathematics, Northwestern University, and Rehabilitation Institute of Chicago, IL</wicri:regionArea><placeName><region type="state">Illinois</region></placeName></affiliation></author></analytic><series><title level="j">Journal of Vision</title><idno type="eISSN">1534-7362</idno><imprint><date when="2014">2014</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>Our brain often needs to estimate unknown variables from imperfect information. Our knowledge about the statistical distributions of quantities in our environment (called priors) and currently available information from sensory inputs (called likelihood) are the basis of all Bayesian models of perception and action. While we know that priors are learned, most studies of prior-likelihood integration simply assume that subjects know about the likelihood. However, as the quality of sensory inputs change over time, we also need to learn about new likelihoods. Here, we show that human subjects readily learn the distribution of visual cues (likelihood function) in a way that can be predicted by models of statistically optimal learning. Using a likelihood that depended on color context, we found that a learned likelihood generalized to new priors. Thus, we conclude that subjects learn about likelihood.</p></div></front></TEI><affiliations><list><country><li>Japon</li><li>États-Unis</li></country><region><li>Illinois</li></region></list><tree><country name="Japon"><noRegion><name sortKey="Sato, Yoshiyuki" sort="Sato, Yoshiyuki" uniqKey="Sato Y" first="Yoshiyuki" last="Sato">Yoshiyuki Sato</name></noRegion></country><country name="États-Unis"><region name="Illinois"><name sortKey="Kording, Konrad P" sort="Kording, Konrad P" uniqKey="Kording K" first="Konrad P." last="Kording">Konrad P. Kording</name></region></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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