Perceptual Multistability as Markov Chain Monte Carlo Inference

NeurIPS 2009 Samuel GershmanEd VulJoshua B. 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... (read more)

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