Perceptual Multistability as Markov Chain Monte Carlo Inference
Samuel J. Gershman, Ed Vul, Joshua B. Tenenbaum · 2009
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 computations algorithmically. We explore the proposal that for some tasks, hu-mans use a form of Markov Chain Monte Carlo to approximate the posterior dis-tribution over hidden variables. As a case study, we show how several phenomena of perceptual multistability can be explained as MCMC inference in simple graph-ical models for low-level vision. 1