Neural Comput - Multistability and perceptual inference.

Tópicos

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{ intervent(3218) particip(2042) group(1664) }
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Resumo

Ambiguous images present a challenge to the visual system: How can uncertainty about the causes of visual inputs be represented when there are multiple equally plausible causes? A Bayesian ideal observer should represent uncertainty in the form of a posterior probability distribution over causes. However, in many real-world situations, computing this distribution is intractable and requires some form of approximation. We argue that the visual system approximates the posterior over underlying causes with a set of samples and that this approximation strategy produces perceptual multistability--stochastic alternation between percepts in consciousness. Under our analysis, multistability arises from a dynamic sample-generating process that explores the posterior through stochastic diffusion, implementing a rational form of approximate Bayesian inference known as Markov chain Monte Carlo (MCMC). We examine in detail the most extensively studied form of multistability, binocular rivalry, showing how a variety of experimental phenomena--gamma-like stochastic switching, patchy percepts, fusion, and traveling waves--can be understood in terms of MCMC sampling over simple graphical models of the underlying perceptual tasks. We conjecture that the stochastic nature of spiking neurons may lend itself to implementing sample-based posterior approximations in the brain.

Resumo Limpo

ambigu imag present challeng visual system can uncertainti caus visual input repres multipl equal plausibl caus bayesian ideal observ repres uncertainti form posterior probabl distribut caus howev mani realworld situat comput distribut intract requir form approxim argu visual system approxim posterior under caus set sampl approxim strategi produc perceptu multistabilitystochast altern percept conscious analysi multist aris dynam samplegener process explor posterior stochast diffus implement ration form approxim bayesian infer known markov chain mont carlo mcmc examin detail extens studi form multist binocular rivalri show varieti experiment phenomenagammalik stochast switch patchi percept fusion travel wavescan understood term mcmc sampl simpl graphic model under perceptu task conjectur stochast natur spike neuron may lend implement samplebas posterior approxim brain

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