Part of Advances in Neural Information Processing Systems 21 (NIPS 2008)
Rama Natarajan, Iain Murray, Ladan Shams, Richard Zemel
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.