title: Bayesian sampling in visual perception.
abstract: It is well-established that some aspects of perception and action can be understood as probabilistic inferences over underlying probability distributions. In some situations, it would be advantageous for the nervous system to sample interpretations from a probability distribution rather than commit to a particular interpretation. In this study, we asked whether visual percepts correspond to samples from the probability distribution over image interpretations, a form of sampling that we refer to as Bayesian sampling. To test this idea, we manipulated pairs of sensory cues in a bistable display consisting of two superimposed moving drifting gratings, and we asked subjects to report their perceived changes in depth ordering. We report that the fractions of dominance of each percept follow the multiplicative rule predicted by Bayesian sampling. Furthermore, we show that attractor neural networks can sample probability distributions if input currents add linearly and encode probability distributions with probabilistic population codes.
authors: Foundation Sant Joan de Déu, Parc Sanitari Sant Joan de Déu, Esplugues de Llobregat, Barcelona, Spain. rmoreno@bcs.rochester.edu
authors: Rubén
authors: R
authors: Moreno-Bote
authors: David C
authors: DC
authors: Knill
authors: Alexandre
authors: A
authors: Pouget
mesh_terms: D001499:Bayes Theorem; D003867:Depth Perception / Q000502:physiology; D023882:Dominance, Ocular / Q000502:physiology; D005260:Female; D006801:Humans; D008297:Male; D008959:Models, Neurological; D015233:Models, Statistical; D009415:Nerve Net / Q000502:physiology; D010775:Photic Stimulation; D014796:Visual Perception / Q000502:physiology*