Monday, December 11, 2006
U. Rochester (Brain & Cognitive Science)
Learning Bayesian Priors for Depth Perception
Pictorial cues to depth rely on prior knowledge about statistical regularities in the environment; for example about the prevalence of symmetric objects, of parallel lines and lighting from above. In the first part of this talk, I will discuss evidence that the human visual system uses probabilistic characterizations for this prior knowledge that incorporate mixtures of multiple possible models of objects. This account explains a number of perceptual effects, most notably non-linear robust cue integration. In the second part of the talk, I will discuss the problem of how the visual system learns the statistical regularities needed to interpret pictorial cues to; in particular, how it adapts its internal model to environments with very different statistics. We specifically tested the hypothesis that the visual system can adapt its model of the statistics of planar figures for estimating 3D surface orientation. Taking elliptical figures as a prototypical case, we develop a Bayesian model that effectively learns the probability density function on shape from stereoscopic images of slanted ellipses. When the model adapts to an irregular environment, it gradually down-weights the pictorial cue to slant provided by the shapes of projected ellipses relative to stereopsis. When estimating surface slant in an environment containing randomly shaped ellipses, human subjects similarly down-weight the pictorial cue over time, but not in an environment containing mostly circles. This shows that they have adapted their internal model of the shape statistics of the environment.