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Representing biological motion

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Thornton,  IM
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Thornton, I., Giese, M., & Edelman, S. (2003). Representing biological motion. Perception, 32(ECVP Abstract Supplement), 34-35.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-DBDE-B
Abstract
Recently, we have been using a novel spatiotemporal morphing algorithm (Giese and Poggio, 2000 International Journal of Computer Vision 38 59 - 73) to explore the mental representation of biological-motion patterns. We presented observers with animated patterns of point lights corresponding to trajectories generated by spatiotemporal morphing between three prototypical human locomotion patterns: walking, running, and marching. The seven patterns obtained from this morphing procedure were organised into stimulus spaces corresponding either to a (T)riangle or an L-shape. For each configuration, observers were asked to judge the similarity between all pairs of stimuli using a comparison-of-pairs paradigm. A measure of perceived dissimilarity computed from these judgments was submitted to multi-dimensional scaling. The perceived structures recovered in this way closely matched the original stimulus structures. This suggests that the visual system represents related categories of locomotion in a common metric space, reflecting the spatiotemporal similarity of motion trajectories. Here, we relate these findings to previous research using static shape categories (eg Cutzu and Edelman, 1998 Vision Research 38 2229 - 2257) and in particular to the results of a recent simulation study. In this latter study, we used the neural responses of a physiologically plausible model of biological-motion recognition (Giese and Poggio, 2003 Nature Reviews Neuroscience 4 179 - 192) to recover metric structures which also closely resembled the original stimulus structure. Implications of these simulation results for our understanding of biological motion processing are discussed.