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Recognising novel deforming objects

MPG-Autoren
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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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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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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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Bülthoff,  HH
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

Externe Ressourcen

https://www.tandfonline.com/doi/full/10.1080/13506280600627756
(Verlagsversion)

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Zitation

Chuang, L., Vuong, Q., Thornton, I., & Bülthoff, H. (2006). Recognising novel deforming objects. Visual Cognition, 14(1), 85-88.


Zitierlink: https://hdl.handle.net/11858/00-001M-0000-0013-D1D5-F
Zusammenfassung
Current theories of visual object recognition tend to focus on static properties, particularly shape. Nonetheless, visual perception is a dynamic experience–as a result of active observers or moving objects. Here, we investigate whether dynamic information can influence visual object-learning. Three learning experiments were conducted that required participants to learn and subsequently recognize different non-rigid objects that deformed over time. Consistent with previous studies of rigid depth-rotation, our results indicate that human observers do represent object-motion. Furthermore, our data suggest that dynamic information could compensate for when static cues are less reliable, for example, as a result of viewpoint variation.