Comparing Bayesian models for multisensory cue combination without mandatory integration
(2008)
Conference Proceeding
Beierholm, U., Kording, K., Shams, L., & Ma, W. (2008). Comparing Bayesian models for multisensory cue combination without mandatory integration. In J. C. Platt, D. Koller, Y. Singer, & S. T. Roweis (Eds.), 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 (81-88)
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 esta... Read More about Comparing Bayesian models for multisensory cue combination without mandatory integration.