Introduction

The previous chapters have demonstrated the many ways one can use a Bayesian formulation for computationally modeling perceptual problems. In this chapter, we look at the implications of a Bayesian view of visual information processing for investigating human visual perception. We will attempt to outline the elements of a general program of empirical research which results from taking the Bayesian formulation seriously as a framework for characterizing human perceptual inference. A major advantage of following such a program is that it supports a strong integration of psychophysics and computational theory, since its structure is the same as that of the Bayesian framework for computational modeling. In particular, it provides the foundation for a psychophysics of constraints, used to test hypotheses about the quantitative and qualitative constraints used in human perceptual inferences. The Bayesian approach also suggests new ways to conceptualize the general problem of perception and to decompose it into isolatable parts for psychophysical investigation. Thus, it not only provides a framework for modeling solutions to specific perceptual problems; it also guides the definition of the problems.

The chapter is organized into four major sections. In the next section, we develop a framework for characterizing human perception in Bayesian terms and analyze its implications for studying human perceptual performance. The third and fourth sections of the chapter apply the framework to two specific problems: the perception of 3-D shape from surface contours and the perception of 3-D object motion from cast shadow motion.