Comparing Bayesian models for multisensory cue combination without mandatory integration

Beierholm, U.R.; Kording, K.P.; Shams, L.; Ma, W.J.

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.

Citation

Beierholm, U., Kording, K., Shams, L., & Ma, W. (2008, December). Comparing Bayesian models for multisensory cue combination without mandatory integration. Presented at 21st Annual Conference on Neural Information Processing Systems 2007, Vancouver, BC

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 21st Annual Conference on Neural Information Processing Systems 2007
• Publication Date: 2008
• Deposit Date: Mar 1, 2016
• Volume: 20
• Pages: 81-88
• Series Title: Advances in Neural Information Processing Systems
• Series ISSN: 1049-5258
• Book Title: Advances in neural information processing systems 20: Proceedings of the 21st Annual Conference on Neural Information Processing Systems 2007; December 3-6, 2007, Vancouver, B.C., Canada.
• Public URL: https://durham-repository.worktribe.com/output/1150992
• Publisher URL: https://papers.nips.cc/paper/3207-comparing-bayesian-models-for-multisensory-cue-combination-without-mandatory-integration