Perceptual Multistability as Markov Chain Monte Carlo Inference

Part of Advances in Neural Information Processing Systems 22 (NIPS 2009)

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Authors

Samuel Gershman, Ed Vul, Joshua Tenenbaum

Abstract

While many perceptual and cognitive phenomena are well described in terms of Bayesian inference, the necessary computations are intractable at the scale of real-world tasks, and it remains unclear how the human mind approximates Bayesian inference algorithmically. We explore the proposal that for some tasks, humans use a form of Markov Chain Monte Carlo to approximate the posterior distribution over hidden variables. As a case study, we show how several phenomena of perceptual multistability can be explained as MCMC inference in simple graphical models for low-level vision.