Transfer of Object Category Knowledge across Visual and Haptic Modalities: Experimental and Computational Studies

Autor/inn/en	Yildirim, Ilker; Jacobs, Robert A.
Titel	Transfer of Object Category Knowledge across Visual and Haptic Modalities: Experimental and Computational Studies
Quelle	In: Cognition, 126 (2013) 2, S.135-148 (14 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0010-0277
DOI	10.1016/j.cognition.2012.08.005
Schlagwörter	Novelty (Stimulus Dimension); Stimuli; Infants; Visual Stimuli; Experiments; Bayesian Statistics; Inferences; Models; Internet + Suchen Sie Ihr Suchwort? Anreizsystem; Infant; Toddler; Toddlers; Kleinkind; Erprobung; Inference; Inferenz; Analogiemodell
Abstract	We study people's abilities to transfer object category knowledge across visual and haptic domains. If a person learns to categorize objects based on inputs from one sensory modality, can the person categorize these same objects when the objects are perceived through another modality? Can the person categorize novel objects from the same categories when these objects are, again, perceived through another modality? Our work makes three contributions. First, by fabricating Fribbles (3-D, multi-part objects with a categorical structure), we developed visual-haptic stimuli that are highly complex and realistic, and thus more ecologically valid than objects that are typically used in haptic or visual-haptic experiments. Based on these stimuli, we developed the "See and Grasp" data set, a data set containing both visual and haptic features of the Fribbles, and are making this data set freely available on the world wide web. Second, complementary to previous research such as studies asking if people transfer knowledge of object identity across visual and haptic domains, we conducted an experiment evaluating whether people transfer object category knowledge across these domains. Our data clearly indicate that we do. Third, we developed a computational model that learns multisensory representations of prototypical 3-D shape. Similar to previous work, the model uses shape primitives to represent parts, and spatial relations among primitives to represent multi-part objects. However, it is distinct in its use of a Bayesian inference algorithm allowing it to acquire multisensory representations, and sensory-specific forward models allowing it to predict visual or haptic features from multisensory representations. The model provides an excellent qualitative account of our experimental data, thereby illustrating the potential importance of multisensory representations and sensory-specific forward models to multisensory perception. (Contains 1 table and 8 figures.) (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10