Identification of Nonrigid 3D Shapes from Motion Cues in the Fovea and Periphery
23.545, Saturday, May 11, 8:30 am - 12:30 pm, Vista Ballroom
Anshul Jain1, Katja Doerschner2, Qasim Zaidi1; 1Graduate Center for Vision Research, SUNY College of Optometry, 2Department of Psychology & National Magnetic Resonance Research Center, Bilkent University
Jain & Zaidi (2011) in a shape-from-motion paradigm showed that human observers are as good at making categorical judgments (fat vs thin) for nonrigid shapes as they are for rigid shapes based solely on motion cues. They showed for the first time that an explicit rigidity assumption was not required for extracting 3D shape from motion cues, at least for simple categorical judgments. In the current study we examined whether a more objective task such as shape identification would reveal an advantage for rigid objects, thus lending support to the rigidity assumption hypothesis. Stimulus consisted of point-light versions of ellipsoids that contained three bumps or dimples at three locations on the surface, thus leading to eight possible shapes. The ellipsoids were either rigid or deformed smoothly in the depth plane or the image plane. The stimuli were presented either at the fovea or at 4 deg. eccentricity after adjusting for the cortical magnification factor. Observers (N=4) performed an 8AFC shape-identification task and we measured their performance as a function of bump/dimple height/depth. Observers performance was identical for rigid and the two nonrigid ellipsoids both at the fovea and under peripheral viewing, thus providing further evidence against an explicit rigidity assumption. Observers performance in the periphery was comparable to their performance in the fovea after adjusting for cortical magnification factor. Our results suggest that extraction of 3D shape from motion cues in the periphery is limited by mechanisms that extract optic flow and lie lower in the visual system hierarchy than by higher level mechanisms that process the optic flow to extract 3D shapes. We propose multi-scale first-order optic flow analyses (div, def and curl) to extract both 3D shapes and deformations.