Comparing Bayesian models for multisensory cue combination without mandatory integration
Abstract
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.
Additional Information
© 2009 Neural Information Processing Systems Foundation.Attached Files
Published - 3207-comparing-bayesian-models-for-multisensory-cue-combination-without-mandatory-integration.pdf
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Additional details
- Eprint ID
- 65749
- Resolver ID
- CaltechAUTHORS:20160329-152742908
- Created
-
2016-03-30Created from EPrint's datestamp field
- Updated
-
2019-10-03Created from EPrint's last_modified field
- Series Name
- Advances in Neural Information Processing Systems
- Series Volume or Issue Number
- 20