How much to trust the senses: Likelihood learning
Identifieur interne : 001493 ( Pmc/Curation ); précédent : 001492; suivant : 001494How 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
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<front><journal-meta><journal-id journal-id-type="nlm-ta">J Vis</journal-id><journal-id journal-id-type="iso-abbrev">J Vis</journal-id><journal-id journal-id-type="hwp">jov</journal-id><journal-id journal-id-type="pmc">jov</journal-id><journal-id journal-id-type="publisher-id">JOV</journal-id><journal-title-group><journal-title>Journal of Vision</journal-title></journal-title-group><issn pub-type="epub">1534-7362</issn><publisher><publisher-name>The Association for Research in Vision and Ophthalmology</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">25398975</article-id><article-id pub-id-type="pmc">4233767</article-id><article-id pub-id-type="doi">10.1167/14.13.13</article-id><article-id pub-id-type="sici">jovi-14-13-08</article-id><article-id pub-id-type="other">MS#: JOV-04161-2014</article-id><article-categories><subj-group subj-group-type="heading"><subject>Articles</subject></subj-group></article-categories><title-group><article-title>How much to trust the senses: Likelihood learning</article-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Sato</surname><given-names>Yoshiyuki</given-names></name><xref ref-type="aff" rid="aff1">1</xref><email>yoshi.yk.sato@gmail.com</email></contrib><contrib contrib-type="author"><name><surname>Kording</surname><given-names>Konrad P.</given-names></name><xref ref-type="aff" rid="aff2">2</xref><email>kk@northwestern.edu</email></contrib><aff id="aff1"><label>1</label>Graduate School of Information Systems, University of Electro-Communications, Japan</aff><aff id="aff2"><label>2</label>Departments of Physical Medicine and Rehabilitation, Physiology, and Applied Mathematics, Northwestern University, and Rehabilitation Institute of Chicago, IL, USA</aff></contrib-group><pub-date pub-type="collection"><year>2014</year></pub-date><pub-date pub-type="epub"><day>14</day><month>11</month><year>2014</year></pub-date><volume>14</volume><issue>13</issue><elocation-id>13</elocation-id><history><date date-type="received"><day>2</day><month>3</month><year>2014</year></date><date date-type="accepted"><day>11</day><month>9</month><year>2014</year></date></history><permissions><copyright-statement>© 2014 ARVO</copyright-statement><copyright-year>2014</copyright-year></permissions><self-uri xlink:title="pdf" xlink:type="simple" xlink:href="i1534-7362-14-13-13.pdf"></self-uri><self-uri xlink:role="icon" xlink:type="simple" xlink:href="14.13.13.gif"></self-uri><abstract><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></abstract><kwd-group><title>Keywords</title><kwd><italic>Bayesian models</italic></kwd><kwd><italic>likelihood learning</italic></kwd><kwd><italic>sensorimotor integration</italic></kwd><kwd><italic>context-dependent learning</italic></kwd></kwd-group></article-meta></front></pmc></record>Pour manipuler ce document sous Unix (Dilib)
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