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.

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. Papers published at the Neural Information Processing Systems Conference.

We have produced a 90-second video (click on this link to view the video) showing a'random walk' (a particular case of a Markov process) evolving over 400,000 steps. Figure 1 below shows the last frame (out of 2,000 frames, each one with 200 new steps). A basic, two-state (going up or down), one-dimensional Markov process is defined as follows: You start at time t 0, walking along the X-axis (representing time). At each iteration (also called step), you move up with probability p, and down with probability q, along the Y-axis. The Y-axis could represent gain/losses in a gamble (throwing a dice), stock market gains etc.