### 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 |

### 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 |

### Fingerprint

### Keywords

- Bayesian methods
- Causal inference
- Visual perception

### ASJC Scopus subject areas

- Information Systems

### Cite this

*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.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*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.

}

TY - GEN

T1 - Comparing Bayesian models for multisensory cue combination without mandatory integration

AU - Beierholm, Ulrik R.

AU - Körding, Konrad P.

AU - Shams, Ladan

AU - Ma, Wei Ji

PY - 2009

Y1 - 2009

N2 - 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.

AB - 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.

KW - Bayesian methods

KW - Causal inference

KW - Visual perception

UR - http://www.scopus.com/inward/record.url?scp=84858781790&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84858781790&partnerID=8YFLogxK

M3 - Conference contribution

SN - 160560352X

SN - 9781605603520

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

ER -