Effective integration of serially presented stochastic cues
Identifieur interne : 002189 ( Ncbi/Merge ); précédent : 002188; suivant : 002190Effective integration of serially presented stochastic cues
Auteurs : Mordechai Z. Juni [États-Unis] ; Todd M. Gureckis [États-Unis] ; Laurence T. Maloney [États-Unis]Source :
- Journal of Vision [ 1534-7362 ] ; 2012.
Abstract
This study examines how people deal with inherently stochastic cues when estimating a latent environmental property. Seven cues to a hidden location were presented one at a time in rapid succession. The seven cues were sampled from seven different Gaussian distributions that shared a common mean but differed in precision (the reciprocal of variance). The experimental task was to estimate the common mean of the Gaussians from which the cues were drawn. Observers ran in two conditions on separate days. In the “decreasing precision” condition the seven cues were ordered from most precise to least precise. In the “increasing precision” condition this ordering was reversed. For each condition, we estimated the weight that each cue in the sequence had on observers' estimates and compared human performance to that of an ideal observer who maximizes expected gain. We found that observers integrated information from more than one cue, and that they adaptively gave more weight to more precise cues and less weight to less precise cues. However, they did not assign weights that would maximize their expected gain, even over the course of several hundred trials with corrective feedback. The cost to observers of their suboptimal performance was on average 16% of their maximum possible winnings.
Url:
DOI: 10.1167/12.8.12
PubMed: 22911906
PubMed Central: 3556466
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PMC:3556466Le document en format XML
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<front><div type="abstract" xml:lang="en"><p>This study examines how people deal with inherently stochastic cues when estimating a latent environmental property. Seven cues to a hidden location were presented one at a time in rapid succession. The seven cues were sampled from seven different Gaussian distributions that shared a common mean but differed in precision (the reciprocal of variance). The experimental task was to estimate the common mean of the Gaussians from which the cues were drawn. Observers ran in two conditions on separate days. In the “decreasing precision” condition the seven cues were ordered from most precise to least precise. In the “increasing precision” condition this ordering was reversed. For each condition, we estimated the weight that each cue in the sequence had on observers' estimates and compared human performance to that of an ideal observer who maximizes expected gain. We found that observers integrated information from more than one cue, and that they adaptively gave more weight to more precise cues and less weight to less precise cues. However, they did not assign weights that would maximize their expected gain, even over the course of several hundred trials with corrective feedback. The cost to observers of their suboptimal performance was on average 16% of their maximum possible winnings.</p>
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<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">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>
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<issn pub-type="epub">1534-7362</issn>
<publisher><publisher-name>The Association for Research in Vision and Ophthalmology</publisher-name>
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<article-id pub-id-type="doi">10.1167/12.8.12</article-id>
<article-id pub-id-type="sici">jovi-12-07-30</article-id>
<article-id pub-id-type="other">JOV-02723-2011.R1</article-id>
<article-categories><subj-group subj-group-type="heading"><subject>Articles</subject>
</subj-group>
</article-categories>
<title-group><article-title>Effective integration of serially presented stochastic cues</article-title>
<alt-title alt-title-type="runhead">Effective cue integration</alt-title>
</title-group>
<contrib-group><contrib contrib-type="author"><name><surname>Juni</surname>
<given-names>Mordechai Z.</given-names>
</name>
<xref ref-type="aff" rid="aff1">1</xref>
<email>mjuni@nyu.edu</email>
<uri xlink:type="simple" xlink:href="http://files.nyu.edu/mzj203/public/site/Mordechai.html">http://files.nyu.edu/mzj203/public/site/Mordechai.html</uri>
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<contrib contrib-type="author"><name><surname>Gureckis</surname>
<given-names>Todd M.</given-names>
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<email>todd.gureckis@nyu.edu</email>
<uri xlink:type="simple" xlink:href="http://gureckislab.org">http://gureckislab.org</uri>
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<contrib contrib-type="author"><name><surname>Maloney</surname>
<given-names>Laurence T.</given-names>
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<xref ref-type="aff" rid="aff1">1</xref>
<xref ref-type="aff" rid="aff2">2</xref>
<email>ltm1@nyu.edu</email>
<uri xlink:type="simple" xlink:href="http://www.psych.nyu.edu/maloney">http://www.psych.nyu.edu/maloney</uri>
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<aff id="aff1">Department of Psychology, New York University, New York, NY, USA</aff>
<aff id="aff2">Center for Neural Science, New York University, New York, NY, USA</aff>
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<pub-date pub-type="collection"><year>2012</year>
</pub-date>
<pub-date pub-type="epub"><day>21</day>
<month>8</month>
<year>2012</year>
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<volume>12</volume>
<issue>8</issue>
<elocation-id>12</elocation-id>
<history><date date-type="received"><day>7</day>
<month>8</month>
<year>2011</year>
</date>
<date date-type="accepted"><day>18</day>
<month>6</month>
<year>2012</year>
</date>
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<permissions><copyright-statement>© 2012 ARVO</copyright-statement>
<copyright-year>2012</copyright-year>
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<abstract><p>This study examines how people deal with inherently stochastic cues when estimating a latent environmental property. Seven cues to a hidden location were presented one at a time in rapid succession. The seven cues were sampled from seven different Gaussian distributions that shared a common mean but differed in precision (the reciprocal of variance). The experimental task was to estimate the common mean of the Gaussians from which the cues were drawn. Observers ran in two conditions on separate days. In the “decreasing precision” condition the seven cues were ordered from most precise to least precise. In the “increasing precision” condition this ordering was reversed. For each condition, we estimated the weight that each cue in the sequence had on observers' estimates and compared human performance to that of an ideal observer who maximizes expected gain. We found that observers integrated information from more than one cue, and that they adaptively gave more weight to more precise cues and less weight to less precise cues. However, they did not assign weights that would maximize their expected gain, even over the course of several hundred trials with corrective feedback. The cost to observers of their suboptimal performance was on average 16% of their maximum possible winnings.</p>
</abstract>
<kwd-group><title>Keywords</title>
<kwd>cue integration</kwd>
<kwd>effective cue integration</kwd>
<kwd>learning cue precisions</kwd>
<kwd>sequential integration</kwd>
<kwd>stochastic cues</kwd>
<kwd>visual estimation</kwd>
</kwd-group>
</article-meta>
</front>
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<region><li>État de New York</li>
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<name sortKey="Gureckis, Todd M" sort="Gureckis, Todd M" uniqKey="Gureckis T" first="Todd M." last="Gureckis">Todd M. Gureckis</name>
<name sortKey="Maloney, Laurence T" sort="Maloney, Laurence T" uniqKey="Maloney L" first="Laurence T." last="Maloney">Laurence T. Maloney</name>
<name sortKey="Maloney, Laurence T" sort="Maloney, Laurence T" uniqKey="Maloney L" first="Laurence T." last="Maloney">Laurence T. Maloney</name>
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