Probabilistic combination of slant information: Weighted averaging and robustness as optimal percepts
Identifieur interne : 000F04 ( Pmc/Curation ); précédent : 000F03; suivant : 000F05Probabilistic combination of slant information: Weighted averaging and robustness as optimal percepts
Auteurs : Ahna R. Girshick ; Martin S. BanksSource :
- Journal of vision [ 1534-7362 ] ; 2009.
Abstract
Depth perception involves combining multiple, possibly conflicting, sensory measurements to estimate the 3D structure of the viewed scene. Previous work has shown that the perceptual system combines measurements using a statistically optimal weighted average. However, the system should only combine measurements when they come from the same source. We asked whether the brain avoids combining measurements when they differ from one another: that is, whether the system is robust to outliers. To do this, we investigated how two slant cues—binocular disparity and texture gradients—influence perceived slant as a function of the size of the conflict between the cues. When the conflict was small, we observed weighted averaging. When the conflict was large, we observed robust behavior: perceived slant was dictated solely by one cue, the other being rejected. Interestingly, the rejected cue was either disparity or texture, and was not necessarily the more variable cue. We modeled the data in a probabilistic framework, and showed that weighted averaging and robustness are predicted if the underlying likelihoods have heavier tails than Gaussians. We also asked whether observers had conscious access to the single-cue estimates when they exhibited robustness and found they did not, i.e. they completely fused despite the robust percepts.
Url:
DOI: 10.1167/9.9.8
PubMed: 19761341
PubMed Central: 2940417
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<front><div type="abstract" xml:lang="en"><p id="P1">Depth perception involves combining multiple, possibly conflicting, sensory measurements to estimate the 3D structure of the viewed scene. Previous work has shown that the perceptual system combines measurements using a statistically optimal weighted average. However, the system should only combine measurements when they come from the same source. We asked whether the brain avoids combining measurements when they differ from one another: that is, whether the system is robust to outliers. To do this, we investigated how two slant cues—binocular disparity and texture gradients—influence perceived slant as a function of the size of the conflict between the cues. When the conflict was small, we observed weighted averaging. When the conflict was large, we observed robust behavior: perceived slant was dictated solely by one cue, the other being rejected. Interestingly, the rejected cue was either disparity or texture, and was not necessarily the more variable cue. We modeled the data in a probabilistic framework, and showed that weighted averaging and robustness are predicted if the underlying likelihoods have heavier tails than Gaussians. We also asked whether observers had conscious access to the single-cue estimates when they exhibited robustness and found they did not, i.e. they completely fused despite the robust percepts.</p>
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<contrib-group><contrib contrib-type="author"><name><surname>Girshick</surname>
<given-names>Ahna R.</given-names>
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<email>ahna@cns.nyu.edu</email>
<aff id="A1">Department of Psychology and Center for Neural Science, New York University, New York, NY, USA</aff>
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<contrib contrib-type="author"><name><surname>Banks</surname>
<given-names>Martin S.</given-names>
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<email>martybanks@berkeley.edu</email>
<aff id="A2">Vision Science Program, Department of Psychology and Helen Wills Neuroscience Institute, University of California, Berkeley, CA, USA</aff>
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<author-notes><corresp id="cor1">Corresponding author: Ahna R. Girshick. <email>ahna@cns.nyu.edu</email>
. Address: 6 Washington Place, New York, NY 10003, USA</corresp>
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<pub-date pub-type="nihms-submitted"><day>24</day>
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<month>8</month>
<year>2009</year>
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<pub-date pub-type="pmc-release"><day>16</day>
<month>9</month>
<year>2010</year>
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<volume>9</volume>
<issue>9</issue>
<fpage>8.1</fpage>
<lpage>820</lpage>
<abstract><p id="P1">Depth perception involves combining multiple, possibly conflicting, sensory measurements to estimate the 3D structure of the viewed scene. Previous work has shown that the perceptual system combines measurements using a statistically optimal weighted average. However, the system should only combine measurements when they come from the same source. We asked whether the brain avoids combining measurements when they differ from one another: that is, whether the system is robust to outliers. To do this, we investigated how two slant cues—binocular disparity and texture gradients—influence perceived slant as a function of the size of the conflict between the cues. When the conflict was small, we observed weighted averaging. When the conflict was large, we observed robust behavior: perceived slant was dictated solely by one cue, the other being rejected. Interestingly, the rejected cue was either disparity or texture, and was not necessarily the more variable cue. We modeled the data in a probabilistic framework, and showed that weighted averaging and robustness are predicted if the underlying likelihoods have heavier tails than Gaussians. We also asked whether observers had conscious access to the single-cue estimates when they exhibited robustness and found they did not, i.e. they completely fused despite the robust percepts.</p>
</abstract>
<kwd-group><kwd>cue combination</kwd>
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<kwd>sensory fusion</kwd>
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<contract-num rid="EY1">R01 EY012851-09
||EY</contract-num>
<contract-num rid="EY1">R01 EY012851-08
||EY</contract-num>
<contract-sponsor id="EY1">National Eye Institute : NEI</contract-sponsor>
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