Comparing Bayesian models for multisensory cue combination without mandatory integration

Ulrik R. Beierholm, Konrad P. Körding, Ladan Shams, Wei Ji Ma

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationAdvances in Neural Information Processing Systems 20 - Proceedings of the 2007 Conference
StatePublished - 2009
Event21st Annual Conference on Neural Information Processing Systems, NIPS 2007 - Vancouver, BC, Canada
Duration: Dec 3 2007Dec 6 2007

Other

Other21st Annual Conference on Neural Information Processing Systems, NIPS 2007
CountryCanada
CityVancouver, BC
Period12/3/0712/6/07

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Brain
Experiments

Keywords

  • Bayesian methods
  • Causal inference
  • Visual perception

ASJC Scopus subject areas

  • Information Systems

Cite this

Beierholm, U. R., Körding, K. P., Shams, L., & Ma, W. J. (2009). Comparing Bayesian models for multisensory cue combination without mandatory integration. In Advances in Neural Information Processing Systems 20 - Proceedings of the 2007 Conference

Comparing Bayesian models for multisensory cue combination without mandatory integration. / Beierholm, Ulrik R.; Körding, Konrad P.; Shams, Ladan; Ma, Wei Ji.

Advances in Neural Information Processing Systems 20 - Proceedings of the 2007 Conference. 2009.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Beierholm, UR, Körding, KP, Shams, L & Ma, WJ 2009, Comparing Bayesian models for multisensory cue combination without mandatory integration. in Advances in Neural Information Processing Systems 20 - Proceedings of the 2007 Conference. 21st Annual Conference on Neural Information Processing Systems, NIPS 2007, Vancouver, BC, Canada, 12/3/07.
Beierholm UR, Körding KP, Shams L, Ma WJ. Comparing Bayesian models for multisensory cue combination without mandatory integration. In Advances in Neural Information Processing Systems 20 - Proceedings of the 2007 Conference. 2009
Beierholm, Ulrik R. ; Körding, Konrad P. ; Shams, Ladan ; Ma, Wei Ji. / Comparing Bayesian models for multisensory cue combination without mandatory integration. Advances in Neural Information Processing Systems 20 - Proceedings of the 2007 Conference. 2009.
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