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.Bayesian inference | neuronal network | neuronal noise | perceptual bistability T here is mounting evidence that neural circuits can implement probabilistic inferences over sensory, cognitive, or motor variables. In some cases, humans can perform these inferences optimally, as in multi-cue or multisensory integration (1-8). For complex tasks, such as object recognition, action perception, and object tracking, the computations required for optimal inference are intractable, which implies that humans must use approximate inferences (9-11). One approximate scheme that is particularly appealing from a biological point of view is sampling. Consider as an example the problem of object recognition. The goal of the inference in this case would be to compute the probability over object identities given the image. Although this probability distribution may be difficult to compute explicitly, one can often design algorithms to generate samples from the distribution, allowing one to perform approximate inference (12, 13). Some human cognitive choice behaviors suggest that the nervous system implements sampling. However, whether the same is true for lowlevel perceptual processing is currently unknown.Stimuli that lead to bistable percepts (14-18), like the Necker cube, provide a tractable experimental preparation for testing the sampling hypothesis. With such stimuli, perception alternates stochastically between two possible interpretations, a behavior consistent with sampling as suggested by several works (16,19,20). However, the key question is what probability distribution is being sampled. If the brain uses sampling for Bayesian inference, neural circuits should sample from an internal probability distribution on possible stimulus interpretations that are conditioned on the available sensory data, the so-called posterior distribution. This distribution places important constraints on the distributions of perceptual states for bistable stimuli.To test this idea, we used stimuli composed of two dr...

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