Bayesian decision theory and psychophysics

Abstract

We argue that Bayesian decision theory provides a good theoretical framework for visual perception. Such a theory involves a likelihood function specifying how the scene generates the image(s), a prior assumption about the scene, and a decision rule to determine the scene interpretation. This is illustrated by describing Bayesian theories for individual visual cues and showing that perceptual biases found in psychophysical experiments can be interpreted as biases towards prior assumptions made by the visual system. We then describe the implications of this framework for the integration of different cues. We argue that the dependence of cues on prior assumptions means that care must be taken to model these dependencies during integration. This suggests that a number of proposed schemes for cue integration, which only allow weak interaction between cues, are not adequate and instead stronger coupling is often required. These theories require the choice of decision rules and we argue that this choice is important since these rules help capture the task dependent nature of vision. This is illustrated by analysing the generic viewpoint assumption. Finally, we suggest that the visual system uses a set of competing prior assumptions, rather than the single generic priors, or natural constraints, commonly used in computational theories of vision.