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Bayesian modelling of visual perception

(from the chapter) Bayesian Decision Theory (BDT) has been suggest as a convenient and natural framework that allows researchers to study all aspects of a perceptual decision in a unified manner. This framework involves 3 basic components: the task of the organism, prior knowledge (PK) about the environment, and knowledge of the way the environment is sensed by the organism. This chapter summarizes the key points that make the Bayesian framework attractive as a framework for the study of perception and illustrates how to develop models of visual function based on BDT. It emphasizes the role played by PK about the environment in the interpretation of images and describes how this PK is represented as prior distributions in BDT. To introduce the Bayesian approach, the chapter illustrates how to model a simplified problem of 3-dimensional perception. Following the example, the chapter illustrates how the framework can be used to model slightly more realistic problems concerning the perception of shape from shading and from contours. The chapter concludes with a general discussion of the main issues of the Bayesian approach. (PsycINFO Database Record (c) 2005 APA, all rights reserved)