 The relevance of Bayes’ Theorem to Cognitive Science. Bayesianism is in its origins a normative theory of what one ought to believe under specific epistemic circumstances, and as such it has been applied extensively in understanding theory confirmation in the sciences. It first came to the fore in the cognitive sciences as an ideal against which one could measure human irrationality. Kahneman and Tversky (1972) famously showed that ordinary reasoners typically fall short of Bayesian standards when they are asked to decide the bearing of evidence on hypotheses, in part because they misjudge the relevance of the prior probability of the hypotheses. But in recent years, Bayesian methodologies have become a unifying framework for analyzing all aspects of cognition that can be represented as inference under uncertainty.  For example,  Bayesian ideas have been successfully applied to the processing underlying perception—especially the visual system (Knill &amp; Richards 1996; Rao et al. 2002). In visual perception, a pattern of light hits the eye (the proximal stimulus), and the visual system needs to determine the nature of the visual scene in the environment (the distal stimulus) that caused that pattern. The proximal stimulus is compatible with a number of different distal stimuli. So the system faces something like the under-determination problem that a scientist faces. Both must select one view about what the world is like on the basis of information that still leaves other possibilities open. It turns out that Bayesian methods have been very successful at modeling how the visual system resolves these uncertainties.