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  • Friday, April 11, 2008
    Visual perception from a bayesian perspective

    Visual perception can be seen as an inference problem where the visual system is trying to find the best interpretation in face of uncertain information. Within this context, the Bayesian probabilistic framework has turned out to be a useful tool to make explicit the prior constraints used to complement the retinal information, and more recently, the possible utility functions underlying perceptual decisions. In this talk, I will present five small projects inspired by this Bayesian framework: (1) the assumption on the light source position for perceived shape from shading, (2) the probabilistic nature of bistable perception, (3) the difference between perceived depth and slant from binocular disparities, (4) the cost function for visuo-motor synchrony, and (5) the perceived time of a moving stimulus

    Pascal Mamassian