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Autocorrelated Sampling in Cognition: Implementing MCMC Algorithms as Cognitive Models and Fitting Them Without Likelihoods
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Abstract
One of the fundamental questions in cognitive science is how people achieve such high levels of performance given the very limited resources they have access to. While human behaviour is similar to the Bayesian ideal both in low-level domains such as vision (Yuille & Kersten, 2006) and high-level domains such as categorization (Xu & Tenenbaum, 2007); optimality is in fact impossible given existing constraints.