Giessener Elektronische Bibliothek

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Perceiving animacy from shape

Schmidt, Filipp ; Hegele, Mathias ; Fleming, Roland W.


Originalveröffentlichung: (2017) Journal of Vision 17(11):10 doi: 10.1167/17.11.10
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URN: urn:nbn:de:hebis:26-opus-138159
URL: http://geb.uni-giessen.de/geb/volltexte/2018/13815/


Sammlung: Open Access - Publikationsfonds
Universität Justus-Liebig-Universität GieĂźen
Institut: Department of Psychology
Fachgebiet: Psychologie
DDC-Sachgruppe: Psychologie
Dokumentart: Aufsatz
Sprache: Englisch
Erstellungsjahr: 2017
Publikationsdatum: 06.11.2018
Kurzfassung auf Englisch: Superordinate visual classification—for example, identifying an image as “animal,” “plant,” or “mineral”—is computationally challenging because radically different items (e.g., “octopus,” “dog”) must be grouped into a common class (“animal”). It is plausible that learning superordinate categories teaches us not only the membership of particular (familiar) items, but also general features that are shared across class members, aiding us in classifying novel (unfamiliar) items. Here, we investigated visual shape features associated with animate and inanimate classes. One group of participants viewed images of 75 unfamiliar and atypical items and provided separate ratings of how much each image looked like an animal, plant, and mineral. Results show systematic tradeoffs between the ratings, indicating a class-like organization of items. A second group rated each image in terms of 22 midlevel shape features (e.g., “symmetrical,” “curved”). The results confirm that superordinate classes are associated with particular shape features (e.g., “animals” generally have high “symmetry” ratings). Moreover, linear discriminant analysis based on the 22-D feature vectors predicts the perceived classes approximately as well as the ground truth classification. This suggests that a generic set of midlevel visual shape features forms the basis for superordinate classification of novel objects along the animacy continuum.
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