### 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 language | English (US) |
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Title of host publication | Advances in Neural Information Processing Systems 20 - Proceedings of the 2007 Conference |

State | Published - 2009 |

Event | 21st Annual Conference on Neural Information Processing Systems, NIPS 2007 - Vancouver, BC, Canada Duration: Dec 3 2007 → Dec 6 2007 |

### Publication series

Name | Advances in Neural Information Processing Systems 20 - Proceedings of the 2007 Conference |
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### Other

Other | 21st Annual Conference on Neural Information Processing Systems, NIPS 2007 |
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Country | Canada |

City | Vancouver, BC |

Period | 12/3/07 → 12/6/07 |

### Keywords

- Bayesian methods
- Causal inference
- Visual perception

### ASJC Scopus subject areas

- Information Systems

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## Cite this

*Advances in Neural Information Processing Systems 20 - Proceedings of the 2007 Conference*(Advances in Neural Information Processing Systems 20 - Proceedings of the 2007 Conference).