Measurement of generalization fields for the recognition of biological motion.
The human visual system processes complex biological motion stimuli with high sensitivity and selectivity. The characterization of spatio-temporal generalization in the perception of biological motion is still a largely unresolved problem. We present an experiment that investigates how the visual system responds to motion stimuli that interpolate spatio-temporally between natural biological motion patterns. Inspired by analogous studies in stationary object recognition, we generated stimuli that interpolate between natural perceptual categories by morphing. Spatio-temporal morphs between natural movement patterns were obtained with a technique that allows to calculate linear combinations of spatio-temporal patterns. The weights of such linear combinations define a linear metric space over the set of generated movement patterns, so that the spatio-temporal similarity of the motion patterns can be quantified. In our experiments, we found smooth and continuous variation of the categorization probabilities with the weights of the prototypes in the morphs. For bipedal locomotion patterns we could accurately predict the perceived properties of the morphs by linear combinations of the perceived properties of the prototypes. Such predictions were not possible for morphs between locomotion and very dissimilar movements. We conclude that the visual system shows generalization within classes of motion patterns with similar basic structure, such as bipedal locomotion.
Center for Biological and Computational Learning, Massachusetts Institute of Technology, Cambridge, MA 02139, USA. martin.giese@tuebingen.mpg.de
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D000328:Adult; D001696:Biomechanical Phenomena; D005260:Female; D006801:Humans; D008124:Locomotion; D008297:Male; D009039:Motion Perception / Q000502:physiology*; D011601:Psychophysics