Implicit knowledge of visual uncertainty guides decisions with asymmetric outcomes
Identifieur interne : 002335 ( Pmc/Checkpoint ); précédent : 002334; suivant : 002336Implicit knowledge of visual uncertainty guides decisions with asymmetric outcomes
Auteurs : Louise Whiteley ; Maneesh SahaniSource :
- Journal of vision [ 1534-7362 ] ; 2008.
Abstract
Perception is an “inverse problem,” in which the state of the world must be inferred from the sensory neural activity that results. However, this inference is both ill-posed (
Url:
DOI: 10.1167/8.3.2
PubMed: 18484808
PubMed Central: 2515365
Affiliations:
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<author><name sortKey="Whiteley, Louise" sort="Whiteley, Louise" uniqKey="Whiteley L" first="Louise" last="Whiteley">Louise Whiteley</name>
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<author><name sortKey="Sahani, Maneesh" sort="Sahani, Maneesh" uniqKey="Sahani M" first="Maneesh" last="Sahani">Maneesh Sahani</name>
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<author><name sortKey="Sahani, Maneesh" sort="Sahani, Maneesh" uniqKey="Sahani M" first="Maneesh" last="Sahani">Maneesh Sahani</name>
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<front><div type="abstract" xml:lang="en"><p id="P1">Perception is an “inverse problem,” in which the state of the world must be inferred from the sensory neural activity that results. However, this inference is both ill-posed (<xref ref-type="bibr" rid="R9">Helmholtz, 1856</xref>
; <xref ref-type="bibr" rid="R22">Marr, 1982</xref>
) and corrupted by noise (<xref ref-type="bibr" rid="R7">Green & Swets, 1989</xref>
), requiring the brain to compute perceptual beliefs under conditions of uncertainty. Here we show that human observers performing a simple visual choice task under an externally imposed loss function approach the optimal strategy, as defined by Bayesian probability and decision theory (<xref ref-type="bibr" rid="R1">Berger, 1985</xref>
; <xref ref-type="bibr" rid="R4">Cox, 1961</xref>
). In concert with earlier work, this suggests that observers possess a model of their internal uncertainty and can utilize this model in the neural computations that underlie their behavior (<xref ref-type="bibr" rid="R15">Knill & Pouget, 2004</xref>
). In our experiment, optimal behavior requires that observers integrate the loss function with an estimate of their internal uncertainty rather than simply requiring that they use a modal estimate of the uncertain stimulus. Crucially, they approach optimal behavior even when denied the opportunity to learn adaptive decision strategies based on immediate feedback. Our data thus support the idea that flexible representations of uncertainty are pre-existing, widespread, and can be propagated to decision-making areas of the brain.</p>
</div>
</front>
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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>
<pmc-dir>properties manuscript</pmc-dir>
<front><journal-meta><journal-id journal-id-type="nlm-journal-id">101147197</journal-id>
<journal-id journal-id-type="pubmed-jr-id">30247</journal-id>
<journal-id journal-id-type="nlm-ta">J Vis</journal-id>
<journal-id journal-id-type="iso-abbrev">J Vis</journal-id>
<journal-title-group><journal-title>Journal of vision</journal-title>
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<article-id pub-id-type="doi">10.1167/8.3.2</article-id>
<article-id pub-id-type="manuscript">UKMS1676</article-id>
<article-categories><subj-group subj-group-type="heading"><subject>Article</subject>
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<title-group><article-title>Implicit knowledge of visual uncertainty guides decisions with asymmetric outcomes</article-title>
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<contrib-group><contrib contrib-type="author"><name><surname>Whiteley</surname>
<given-names>Louise</given-names>
</name>
</contrib>
<contrib contrib-type="author"><name><surname>Sahani</surname>
<given-names>Maneesh</given-names>
</name>
</contrib>
<aff id="A1">Gatsby Computational Neuroscience Unit, University College London, London, UK</aff>
</contrib-group>
<author-notes><corresp id="CR1">Corresponding author: Louise Whiteley or Maneesh Sahani. Email: <email>louisew@gatsby.ucl.ac.uk</email>
or <email>maneesh@gatsby.ucl.ac.uk</email>
. Address: Gatsby Computational Neuroscience Unit, University College London, Alexandra House, 17 Queen Square, WC1N 3AR, UK.</corresp>
</author-notes>
<pub-date pub-type="nihms-submitted"><day>21</day>
<month>4</month>
<year>2008</year>
</pub-date>
<pub-date pub-type="epub"><day>06</day>
<month>3</month>
<year>2008</year>
</pub-date>
<pub-date pub-type="pmc-release"><day>13</day>
<month>8</month>
<year>2008</year>
</pub-date>
<volume>8</volume>
<issue>3</issue>
<fpage>2.1</fpage>
<lpage>215</lpage>
<permissions><copyright-statement>© ARVO</copyright-statement>
</permissions>
<abstract><p id="P1">Perception is an “inverse problem,” in which the state of the world must be inferred from the sensory neural activity that results. However, this inference is both ill-posed (<xref ref-type="bibr" rid="R9">Helmholtz, 1856</xref>
; <xref ref-type="bibr" rid="R22">Marr, 1982</xref>
) and corrupted by noise (<xref ref-type="bibr" rid="R7">Green & Swets, 1989</xref>
), requiring the brain to compute perceptual beliefs under conditions of uncertainty. Here we show that human observers performing a simple visual choice task under an externally imposed loss function approach the optimal strategy, as defined by Bayesian probability and decision theory (<xref ref-type="bibr" rid="R1">Berger, 1985</xref>
; <xref ref-type="bibr" rid="R4">Cox, 1961</xref>
). In concert with earlier work, this suggests that observers possess a model of their internal uncertainty and can utilize this model in the neural computations that underlie their behavior (<xref ref-type="bibr" rid="R15">Knill & Pouget, 2004</xref>
). In our experiment, optimal behavior requires that observers integrate the loss function with an estimate of their internal uncertainty rather than simply requiring that they use a modal estimate of the uncertain stimulus. Crucially, they approach optimal behavior even when denied the opportunity to learn adaptive decision strategies based on immediate feedback. Our data thus support the idea that flexible representations of uncertainty are pre-existing, widespread, and can be propagated to decision-making areas of the brain.</p>
</abstract>
<kwd-group><kwd>Bayesian</kwd>
<kwd>early sensory noise</kwd>
<kwd>ideal observer</kwd>
</kwd-group>
<funding-group><award-group><funding-source country="United Kingdom">Wellcome Trust : </funding-source>
<award-id>075098 || WT</award-id>
</award-group>
</funding-group>
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<name sortKey="Whiteley, Louise" sort="Whiteley, Louise" uniqKey="Whiteley L" first="Louise" last="Whiteley">Louise Whiteley</name>
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