Bayesian sampling in visual perception

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On the nature and origin of intuitive theories : learning, physics and psychology (2015, methodology): “MCMC algorithms have recently been proposed as models for the short-timescale dynamics of perceptual inferences in the brain [53, 173, 122], but they are also well-suited to understanding the much longer-term dynamics of learning.”
Theory of cortical function (2017, background): “The noisedriven process of exploration was motivated by stochastic optimization algorithms (57) and is similar to models of sensory neuroscience that draw samples from an underlying probability distribution over possible percepts (9, 10, 39, 43).”
Probabilistic brains: knowns and unknowns (2013, background): “Finally, some groups have proposed that the brain may represent probability distributions by the values of a set of samples drawn from the encoded distribution 34,36,53,54 .”
Theory learning as stochastic search in the language of thought (2012, methodology): “been proposed as models for the short-timescale dynamics of perceptual inferences in the brain (Gershman et al., 2009; Sundareswara & Schrater, 2008; Moreno-Bote et al., 2011), but they are also well-suited to understanding the much longer-term dynamics of learning.”