Characterizing response behavior in multisensory perception with conflicting cues

We explore a recently proposed mixture model approach to understand- ing interactions between conflicting sensory cues. Alternative model for- mulations, differing in their sensory noise models and inference methods, are compared based on their fit to experimental data. Heavy-tailed sen- sory likelihoods yield a better description of the subjects' response behavior than standard Gaussian noise models. We study the underlying cause for this result, and then present several testable predictions of these models.