Perceptual Multistability as Markov Chain Monte Carlo Inference

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

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Samuel Gershman, Ed Vul, Joshua Tenenbaum


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.