Rapid efficient coding of correlated complex acoustic properties
Identifieur interne : 001541 ( Pmc/Curation ); précédent : 001540; suivant : 001542Rapid efficient coding of correlated complex acoustic properties
Auteurs : Christian E. Stilp ; Timothy T. Rogers ; Keith R. KluenderSource :
- Proceedings of the National Academy of Sciences of the United States of America [ 0027-8424 ] ; 2010.
Abstract
Natural sounds are complex, typically changing along multiple acoustic dimensions that covary in accord with physical laws governing sound-producing sources. We report that, after passive exposure to novel complex sounds, highly correlated features initially collapse onto a single perceptual dimension, capturing covariance at the expense of unitary stimulus dimensions. Discriminability of sounds respecting the correlation is maintained, but is temporarily lost for sounds orthogonal or oblique to experienced covariation. Following extended experience, perception of variance not captured by the correlation is restored, but weighted only in proportion to total experienced covariance. A Hebbian neural network model captures some aspects of listener performance; an anti-Hebbian model captures none; but, a principal components analysis model captures the full pattern of results. Predictions from the principal components analysis model also match evolving listener performance in two discrimination tasks absent passive listening. These demonstrations of adaptation to correlated attributes provide direct behavioral evidence for efficient coding.
Url:
DOI: 10.1073/pnas.1009020107
PubMed: 21098293
PubMed Central: 3003067
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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Rapid efficient coding of correlated complex acoustic properties</title><author><name sortKey="Stilp, Christian E" sort="Stilp, Christian E" uniqKey="Stilp C" first="Christian E." last="Stilp">Christian E. Stilp</name></author><author><name sortKey="Rogers, Timothy T" sort="Rogers, Timothy T" uniqKey="Rogers T" first="Timothy T." last="Rogers">Timothy T. Rogers</name></author><author><name sortKey="Kluender, Keith R" sort="Kluender, Keith R" uniqKey="Kluender K" first="Keith R." last="Kluender">Keith R. Kluender</name></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">21098293</idno><idno type="pmc">3003067</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3003067</idno><idno type="RBID">PMC:3003067</idno><idno type="doi">10.1073/pnas.1009020107</idno><date when="2010">2010</date><idno type="wicri:Area/Pmc/Corpus">001541</idno><idno type="wicri:Area/Pmc/Curation">001541</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Rapid efficient coding of correlated complex acoustic properties</title><author><name sortKey="Stilp, Christian E" sort="Stilp, Christian E" uniqKey="Stilp C" first="Christian E." last="Stilp">Christian E. Stilp</name></author><author><name sortKey="Rogers, Timothy T" sort="Rogers, Timothy T" uniqKey="Rogers T" first="Timothy T." last="Rogers">Timothy T. Rogers</name></author><author><name sortKey="Kluender, Keith R" sort="Kluender, Keith R" uniqKey="Kluender K" first="Keith R." last="Kluender">Keith R. Kluender</name></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="2010">2010</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>Natural sounds are complex, typically changing along multiple acoustic dimensions that covary in accord with physical laws governing sound-producing sources. We report that, after passive exposure to novel complex sounds, highly correlated features initially collapse onto a single perceptual dimension, capturing covariance at the expense of unitary stimulus dimensions. Discriminability of sounds respecting the correlation is maintained, but is temporarily lost for sounds orthogonal or oblique to experienced covariation. Following extended experience, perception of variance not captured by the correlation is restored, but weighted only in proportion to total experienced covariance. A Hebbian neural network model captures some aspects of listener performance; an anti-Hebbian model captures none; but, a principal components analysis model captures the full pattern of results. Predictions from the principal components analysis model also match evolving listener performance in two discrimination tasks absent passive listening. These demonstrations of adaptation to correlated attributes provide direct behavioral evidence for efficient coding.</p></div></front></TEI><pmc article-type="research-article"><pmc-comment>The publisher of this article does not allow downloading of the full text in XML form.</pmc-comment>
<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">21098293</article-id><article-id pub-id-type="pmc">3003067</article-id><article-id pub-id-type="publisher-id">201009020</article-id><article-id pub-id-type="doi">10.1073/pnas.1009020107</article-id><article-categories><subj-group subj-group-type="heading"><subject>Biological Sciences</subject><subj-group><subject>Psychological and Cognitive Sciences</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>Rapid efficient coding of correlated complex acoustic properties</article-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Stilp</surname><given-names>Christian E.</given-names></name><xref ref-type="corresp" rid="cor1"><sup>1</sup></xref></contrib><contrib contrib-type="author"><name><surname>Rogers</surname><given-names>Timothy T.</given-names></name></contrib><contrib contrib-type="author"><name><surname>Kluender</surname><given-names>Keith R.</given-names></name></contrib><aff id="aff1">Department of Psychology,<institution>University of Wisconsin</institution>, Madison, WI 53706</aff></contrib-group><author-notes><corresp id="cor1"><sup>1</sup>To whom correspondence should be addressed. E-mail: <email>cestilp@wisc.edu</email>.</corresp><fn fn-type="edited-by"><p>Edited by Wilson S. Geisler, University of Texas at Austin, TX, and approved November 2, 2010 (received for review June 23, 2010)</p></fn><fn fn-type="con"><p>Author contributions: C.E.S., T.T.R., and K.R.K. designed research; C.E.S. performed research; C.E.S., T.T.R., and K.R.K. analyzed data; and C.E.S., T.T.R., and K.R.K. wrote the paper.</p></fn></author-notes><pub-date pub-type="ppub"><day>14</day><month>12</month><year>2010</year></pub-date><pub-date pub-type="epub"><day>22</day><month>11</month><year>2010</year></pub-date><volume>107</volume><issue>50</issue><fpage>21914</fpage><lpage>21919</lpage><self-uri xlink:title="pdf" xlink:type="simple" xlink:href="pnas.201009020.pdf"></self-uri><abstract><p>Natural sounds are complex, typically changing along multiple acoustic dimensions that covary in accord with physical laws governing sound-producing sources. We report that, after passive exposure to novel complex sounds, highly correlated features initially collapse onto a single perceptual dimension, capturing covariance at the expense of unitary stimulus dimensions. Discriminability of sounds respecting the correlation is maintained, but is temporarily lost for sounds orthogonal or oblique to experienced covariation. Following extended experience, perception of variance not captured by the correlation is restored, but weighted only in proportion to total experienced covariance. A Hebbian neural network model captures some aspects of listener performance; an anti-Hebbian model captures none; but, a principal components analysis model captures the full pattern of results. Predictions from the principal components analysis model also match evolving listener performance in two discrimination tasks absent passive listening. These demonstrations of adaptation to correlated attributes provide direct behavioral evidence for efficient coding.</p></abstract><kwd-group><kwd>auditory perception</kwd><kwd>cortical models</kwd><kwd>perceptual organization</kwd></kwd-group></article-meta></front></pmc></record>Pour manipuler ce document sous Unix (Dilib)
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