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Bayesian priors are encoded independently from likelihoods in human multisensory perception

Beierholm, Ulrik R. and Quartz, Steven R. and Shams, Ladan (2009) Bayesian priors are encoded independently from likelihoods in human multisensory perception. Journal of Vision, 9 (5). Art. No. 23. ISSN 1534-7362.

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It has been shown that human combination of crossmodal information is highly consistent with an optimal Bayesian model performing causal inference. These findings have shed light on the computational principles governing crossmodal integration/segregation. Intuitively, in a Bayesian framework priors represent a priori information about the environment, i.e., information available prior to encountering the given stimuli, and are thus not dependent on the current stimuli. While this interpretation is considered as a defining characteristic of Bayesian computation by many, the Bayes rule per se does not require that priors remain constant despite significant changes in the stimulus, and therefore, the demonstration of Bayes-optimality of a task does not imply the invariance of priors to varying likelihoods. This issue has not been addressed before, but here we empirically investigated the independence of the priors from the likelihoods by strongly manipulating the presumed likelihoods (by using two drastically different sets of stimuli) and examining whether the estimated priors change or remain the same. The results suggest that the estimated prior probabilities are indeed independent of the immediate input and hence, likelihood.

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Additional Information:© 2009 Association for Research in Vision and Ophthalmology. Received December 19, 2008; published May 21, 2009. We thank Konrad Koerding, Wei Ji Ma, Stefan Schaal and Peter Bossaerts for their insightful discussions and comments. We also wish to thank the anonymous reviewers for some very useful comments. U.B. and S.Q. were supported by the David and Lucile Packard Foundation and the Betty and Gordon Moore Foundation. L.S. was supported by UCLA Faculty Grants Program and Career Development Program.
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David and Lucile Packard FoundationUNSPECIFIED
Gordon and Betty Moore FoundationUNSPECIFIED
University of California Los Angeles (UCLA)UNSPECIFIED
Subject Keywords:Bayesian inference; causal inference; priors; likelihoods
Issue or Number:5
Record Number:CaltechAUTHORS:20110318-145132559
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Official Citation:Bayesian priors are encoded independently from likelihoods in human multisensory perception Ulrik R. Beierholm, Steven R. Quartz, and Ladan Shams J Vis May 21, 2009 9(5): 23; doi:10.1167/9.5.23
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:22989
Deposited By: Ruth Sustaita
Deposited On:22 Mar 2011 21:39
Last Modified:03 Oct 2019 02:42

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