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Human perception and recognition of metric changes of part-based dynamic novel objects

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Vuong,  QC
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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Schultz,  J
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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Chuang,  L
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

Vuong, Q., Schultz, J., & Chuang, L. (2006). Human perception and recognition of metric changes of part-based dynamic novel objects. Poster presented at 29th European Conference on Visual Perception (ECVP 2006), St. Petersburg, Russia.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-D0AB-8
Abstract
The role of object parts is a key issue in object recognition. Here we investigated whether observers encode qualitative (eg straight versus curved part) or metric information of parts (eg curvature magnitude), and whether the information that is encoded can be affected by motion. To address these issues, we constructed a novel set of objects composed of parts that can vary metrically along different dimensions (eg tapering and bending) to create qualitatively different parts. In a same/different matching task, we presented two objects rigidly rotating in the same or different direction, and had observers judge whether these objects were the same or different. We varied the pair of objects along an ‘identity‘ axis by morphing between two exemplars. A cumulative Gaussian function explained the effect of morph level, suggesting that observers encoded metric information. There was a slight shift of the psychometric function for same versus different motion. Overall, our results suggest that observers are sensitive to metric information, even for objects with salient part structure. We are currently investigating with fMRI how object parts and motion influence neuronal object processing.