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Insights into the perception of 3-D deforming shapes and shape deformations from comparisons of foveal and peripheral performance

43.315, Monday, 19-May, 8:30 am - 12:30 pm, Jacaranda Hall
Session: 3D Perception: Shape from X

Anshul Jain1, Qasim Zaidi1; 1Graduate Center for Vision Research, SUNY College of Optometry

Optic flow patterns have been identified as the primary cues in extracting 3-D shape features (Jain & Zaidi, PNAS 2011), deformations (Jain & Zaidi, JOV 2011) and material properties (Doerschner et al., Current Biology 2011) from motion signals. These patterns can be parsed into combinations of motion divergence and shear, which in turn have been linked to 3-D shape features and deformations (Koenderink and Van Doorn, 1975), and which can selectively activate MT/MST cells. We measured human performance on identification of nonrigid shapes, classification of deformations, and detection of shear and divergence motion patterns. We compared performance on foveal versus 4 degrees peripheral stimuli with a cortical magnification factor of 2.09. In Experiment 1, observers performed an 8AFC shape identification task on point-light ellipsoidal 3-D shapes with three Gaussian features (indentations or projections), and we estimated identification thresholds as a function of indentation/projection height. Performance was similar for rigid and nonrigid shapes, but was better at fovea than at periphery. In Experiment 2, observers performed a 3AFC deformation classification task on horizontal point-light cylinders that were either rigid or flexed nonrigidly along depth or in the image plane. Observers were consistently better at identifying cylinders that flexed in the image-plane than those that flexed in depth. Surprisingly, their performance was better in the periphery than at the fovea for both nonrigidities. In Experiment 3, observers’ performance was similar in the fovea and periphery for both shear and divergence patterns, indicating that the magnification factor was successful in equating sensitivity for the elementary patterns, but not for shape or deformation identification. Sensitivities to combinations of motion patterns cannot thus be predicted from sensitivities to elementary motion patterns alone. These results suggest that estimating 3-D shapes and deformations may involve heuristics that employ non-linear functions or derivatives of the elementary motion patterns.

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