The probabilistic mind : prospects for a Bayesian cognitive science

This chapter introduces the probabilistic approach to cognition; describes the different levels of explanation at which it can apply; reviewes past work; and considers potential challenges to the probabilistic approach. The approach has been widely applied in the areas of perception, motor control, and language, where the performance of dedicated computational modules exceeds the abilities of any artificial computational methods by an enormous margin. Theories of perception based on decorrelation and information compression can be viewed as part of the Bayesian probabilistic approach to perception. The study of perceptuo-motor control provides a second important area of Bayesian analysis. A wide range of experimental evidence has indicated that movement trajectories are accurately predictable. Bayesian decision theory has been widely applied as theoretical framework for understanding the control of movement