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NIPS
2007

Comparing Bayesian models for multisensory cue combination without mandatory integration

8 years 7 months ago
Comparing Bayesian models for multisensory cue combination without mandatory integration
Bayesian models of multisensory perception traditionally address the problem of estimating an underlying variable that is assumed to be the cause of the two sensory signals. The brain, however, has to solve a more general problem: it also has to establish which signals come from the same source and should be integrated, and which ones do not and should be segregated. In the last couple of years, a few models have been proposed to solve this problem in a Bayesian fashion. One of these has the strength that it formalizes the causal structure of sensory signals. We first compare these models on a formal level. Furthermore, we conduct a psychophysics experiment to test human performance in an auditory-visual spatial localization task in which integration is not mandatory. We find that the causal Bayesian inference model accounts for the data better than other models.
Ulrik Beierholm, Konrad P. Körding, Ladan Sha
Added 30 Oct 2010
Updated 30 Oct 2010
Type Conference
Year 2007
Where NIPS
Authors Ulrik Beierholm, Konrad P. Körding, Ladan Shams, Wei Ji Ma
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