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Exploring visual and haptic object categorization

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Gaissert,  N
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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Wallraven,  C
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;

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Citation

Gaissert, N., Wallraven, C., & Bülthoff, H. (2009). Exploring visual and haptic object categorization. Perception, 38(ECVP Abstract Supplement), 159.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-C3C5-4
Abstract
Humans combine visual and haptic shape information in object processing. To investigate commonalities and differences of these two modalities for object categorization, we performed similarity ratings and three different categorization tasks visually and haptically and compared them using multidimensional scaling techniques. As stimuli we used a 3-D object space, of 21 complex parametrically-defined shell-like objects. For haptic experiments, 3-D plastic models were freely explored by blindfolded participants with both hands. For visual experiments, 2-D images of the objects were used. In the first task, we gathered pair-wise similarity ratings for all objects. In the second, unsupervised task, participants freely categorized the objects. In the third, semi-supervised task, participants had to form exactly three groups. In the fourth, supervised task, participants learned three prototype objects and had to assign all other objects accordingly. For all tasks we found that within-category distances were smaller than across-category distances. Categories form clusters in perceptual space with increasing density from unsupervised to supervised categorization. In addition, the unconstrained similarity ratings predict the categorization behavior of the unsupervised categorization task best. Importantly, we found no differences between the modalities in any task showing that the processes underlying categorization are highly similar in vision and haptics.