Cognitive Science An Introduction to the Science of the Mind

This chapter looks at Bayesian approaches to cognitive science. The first section reviews the basic elements of conditional probability and Bayes's rule. The second section explores how Bayesian inference might work in the case of perception, which continuously predicts the outside environment. Sensory inputs provide the evidence so that the perception system derives the conditional probability of different hypotheses, given the current evidence, through Bayes's rule, which allows the perception system to update its hypothesis about the environment. We will look at the case of binocular rivalry to see how this inference can work on ambiguous stimuli. In the next section, we address an extension of Bayesian principles to decision-making -- the theory of expected utility. Utility represents the strength of preference for available options. We introduce the calculation of expected utility and look at some experiments suggesting that the brain processes expected utility in a broadly Bayesian manner.