title: Shading Beats Binocular Disparity in Depth from Luminance Gradients: Evidence against a Maximum Likelihood Principle for Cue Combination.
abstract: Perceived depth is conveyed by multiple cues, including binocular disparity and luminance shading. Depth perception from luminance shading information depends on the perceptual assumption for the incident light, which has been shown to default to a diffuse illumination assumption. We focus on the case of sinusoidally corrugated surfaces to ask how shading and disparity cues combine defined by the joint luminance gradients and intrinsic disparity modulation that would occur in viewing the physical corrugation of a uniform surface under diffuse illumination. Such surfaces were simulated with a sinusoidal luminance modulation (0.26 or 1.8 cy/deg, contrast 20%-80%) modulated either in-phase or in opposite phase with a sinusoidal disparity of the same corrugation frequency, with disparity amplitudes ranging from 0'-20'. The observers' task was to adjust the binocular disparity of a comparison random-dot stereogram surface to match the perceived depth of the joint luminance/disparity-modulated corrugation target. Regardless of target spatial frequency, the perceived target depth increased with the luminance contrast and depended on luminance phase but was largely unaffected by the luminance disparity modulation. These results validate the idea that human observers can use the diffuse illumination assumption to perceive depth from luminance gradients alone without making an assumption of light direction. For depth judgments with combined cues, the observers gave much greater weighting to the luminance shading than to the disparity modulation of the targets. The results were not well-fit by a Bayesian cue-combination model weighted in proportion to the variance of the measurements for each cue in isolation. Instead, they suggest that the visual system uses disjunctive mechanisms to process these two types of information rather than combining them according to their likelihood ratios.
authors.affiliation: Department of Psychology, National Taiwan University, Taipei, Taiwan; Center for Neurobiology and Cognitive Science, National Taiwan University, Taipei, Taiwan.
authors.forename: Chien-Chung
authors.initials: CC
authors.lastname: Chen
authors.affiliation: Smith-Kettlewell Eye Research Institute, San Francisco, California, United States of America; Division of Optometry and Visual Science, School of Health Sciences, City University, London, United Kingdom.
authors.forename: Christopher William
authors.initials: CW
authors.lastname: Tyler
mesh_terms: D001499:Bayes Theorem; D015350:Contrast Sensitivity; D003463:Cues; D003867:Depth Perception; D006801:Humans; D021621:Imaging, Three-Dimensional; D008027:Light; D016013:Likelihood Functions; D015588:Observer Variation; D010364:Pattern Recognition, Visual; D010775:Photic Stimulation / Q000379:methods; D015357:Vision Disparity; D015348:Vision, Binocular