Kramer, Robin S.S., Young, A.W., Day, M.G. and Burton, A.M. (2017) Robust social categorization emerges from learning the identities of very few faces. Psychological Review, 124 (2). pp. 115-129. ISSN 0033-295X
Full content URL: https://doi.org/10.1037/rev0000048
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LDA_paper_R2.pdf - Whole Document 243kB |
Item Type: | Article |
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Item Status: | Live Archive |
Abstract
Viewers are highly accurate at recognizing sex and race from faces-though it remains unclear how this is achieved. Recognition of familiar faces is also highly accurate across a very large range of viewing conditions, despite the difficulty of the problem. Here we show that computation of sex and race can emerge incidentally from a system designed to compute identity. We emphasize the role of multiple encounters with a small number of people, which we take to underlie human face learning. We use highly variable everyday 'ambient' images of a few people to train a Linear Discriminant Analysis (LDA) model on identity. The resulting model has human-like properties, including a facility to cohere previously unseen ambient images of familiar (trained) people-an ability which breaks down for the faces of unknown (untrained) people. The first dimension created by the identity-trained LDA classifies both familiar and unfamiliar faces by sex, and the second dimension classifies faces by race- even though neither of these categories was explicitly coded at learning. By varying the numbers and types of face identities on which a further series of LDA models were trained, we show that this incidental learning of sex and race reflects covariation between these social categories and face identity, and that a remarkably small number of identities need be learnt before such incidental dimensions emerge. The task of learning to recognize familiar faces is sufficient to create certain salient social categories.
Keywords: | Face learning, Face recognition, Social categorization |
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Subjects: | C Biological Sciences > C800 Psychology C Biological Sciences > C850 Cognitive Psychology |
Divisions: | College of Social Science > School of Psychology |
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ID Code: | 29098 |
Deposited On: | 27 Oct 2017 10:51 |
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