A Bayesian Nonparametric Approach to Multisensory Perception


We propose a Bayesian nonparametric model of multisensory perception based upon the Indian buffet process. The model includes a set of latent variables that learn multisensory features from unisensory data. The model is highly flexible because it makes few statistical assumptions. In particular, the number of latent multisensory features is not fixed a priori. Instead, this number is estimated from the observed data. We applied the model to a real-world visual-auditory data set obtained when people spoke English digits. Our results are consistent with several hypotheses about multisensory perception from the cognitive neuroscience literature. We found that the model obtained the statistical advantages provided by sensory integration. We also found that the model acquired multisensory representations that were relatively sensory invariant. Lastly, we found that the model was able to associate unisensory representations based on different modalities.

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