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Touching on face space: Comparing visual and haptic processing of face shapes

Overview of attention for article published in Psychonomic Bulletin & Review, January 2014
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Title
Touching on face space: Comparing visual and haptic processing of face shapes
Published in
Psychonomic Bulletin & Review, January 2014
DOI 10.3758/s13423-013-0577-y
Pubmed ID
Authors

Christian Wallraven

Abstract

The idea that faces are represented within a structured face space (Valentine Quarterly Journal of Experimental Psychology 43: 161-204, 1991) has gained considerable experimental support, from both physiological and perceptual studies. Recent work has also shown that faces can even be recognized haptically-that is, from touch alone. Although some evidence favors congruent processing strategies in the visual and haptic processing of faces, the question of how similar the two modalities are in terms of face processing remains open. Here, this question was addressed by asking whether there is evidence for a haptic face space, and if so, how it compares to visual face space. For this, a physical face space was created, consisting of six laser-scanned individual faces, their morphed average, 50 %-morphs between two individual faces, as well as 50 %-morphs of the individual faces with the average, resulting in a set of 19 faces. Participants then rated either the visual or haptic pairwise similarity of the tangible 3-D face shapes. Multidimensional scaling analyses showed that both modalities extracted perceptual spaces that conformed to critical predictions of the face space framework, hence providing support for similar processing of complex face shapes in haptics and vision. Despite the overall similarities, however, systematic differences also emerged between the visual and haptic data. These differences are discussed in the context of face processing and complex-shape processing in vision and haptics.

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Geographical breakdown

Country Count As %
Unknown 14 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 4 29%
Professor 3 21%
Student > Doctoral Student 2 14%
Student > Ph. D. Student 2 14%
Researcher 2 14%
Other 0 0%
Unknown 1 7%
Readers by discipline Count As %
Psychology 7 50%
Computer Science 2 14%
Business, Management and Accounting 2 14%
Engineering 2 14%
Neuroscience 1 7%
Other 0 0%