Learning Bayesian priors for depth perception.

How the visual system learns the statistical regularities (e.g., symmetry) needed to interpret pictorial cues to depth is one of the outstanding questions in perceptual science. We test the hypothesis that the visual system can adapt its model of the statistics of planar figures for estimating three-dimensional surface orientation. In particular, we test whether subjects, when placed in an environment containing a large proportion of randomly shaped ellipses, learn to give less weight to a prior bias to interpret ellipses as slanted circles when making slant judgments of stereoscopically viewed ellipses. In a first experiment, subjects placed a cylinder onto a stereoscopically viewed, slanted, elliptical surface. In this experiment, subjects received full haptic feedback about the true orientation of the surface at the end of the movement. When test stimuli containing small conflicts between the circle interpretation as figure and the slant suggested by stereoscopic disparities were intermixed with stereoscopically viewed circles, subjects gave the same weight to the circle interpretation over the course of five daily sessions. When the same test stimuli were intermixed with stereoscopic views of randomly shaped ellipses, however, subjects gave progressively lower weights to the circle interpretation of test stimuli over five daily sessions. In a second experiment, subjects showed the same effect when they made perceptual judgments of slant without receiving feedback, showing that feedback is not required for learning. We describe a Bayesian model for combining multiple visual cues to adapt the priors underlying pictorial depth cues that qualitatively accounts for the observed behavior.