Decision-theoretic models of visual perception and action

Abstract

Statistical decision theory (SDT) and Bayesian decision theory (BDT) are closely related mathematical frameworks used to model ideal performance in a wide range of visual and motor tasks. Their elements (gain function, likelihood, prior) are readily interpretable in terms of information available to the observer. We briefly describe SDT and BDT and then review recent work employing them as models of biological perception or action. We emphasize work that employs gain functions and priors as independent or dependent variables. At one extreme, Bayesian decision theory allows the experimenter to compute ideal performance in specific tasks and compare human performance to ideal (Geisler, 1989). No claim is made that visual processing is in any sense "Bayesian". At the other extreme, researchers have proposed Bayesian decision theory as a process model of "perception as Bayesian inference" (Knill & Richards, 1996). We end by discussing how possible ideal models are related to imperfect, actual observers and how the "Bayesian hypothesis" can be tested experimentally.