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The Bayesian approach to perception focuses on whether the computations that the brain performs in perceiving the world can be described as forms of Bayesian inference. Bayesian inference is the process of optimally extracting information from noisy inputs and, if needed, combining this information with prior knowledge. The Bayesian approach to perception typically involves mathematically precise modeling of perceptual experiments in humans. Central to Bayesian inference is Bayes' rule (also called Bayes' theorem), named after the 18th-century British mathematician and Presbyterian minister Thomas Bayes. Bayes' rule is a general equation directly derived from the basic tenets of probability theory, but in the context of perception, it is based on the fact that the brain's observations of physical stimuli (such as the orientation of a line ...

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