Psychological trait inferences from women’s clothing: human and machine prediction

Hannes Rosenbusch (), Maya Aghaei, Anthony M. Evans and Marcel Zeelenberg
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Hannes Rosenbusch: Tilburg University
Maya Aghaei: Istituto Italiano di Tecnologia
Anthony M. Evans: Tilburg University
Marcel Zeelenberg: Tilburg University

Journal of Computational Social Science, 2021, vol. 4, issue 2, No 3, 479-501

Abstract: Abstract People use clothing to make personality inferences about others, and these inferences steer social behaviors. The current work makes four contributions to the measurement and prediction of clothing-based person perception: first, we integrate published research and open-ended responses to identify common psychological inferences made from clothes (Study 1). We find that people use clothes to make inferences about happiness, sexual interest, intelligence, trustworthiness, and confidence. Second, we examine consensus (i.e., interrater agreement) for clothing-based inferences (Study 2). We observe that characteristics of the inferring observer contribute more to the drawn inferences than the observed clothes, which entails low to medium levels of interrater agreement. Third, the current work examines whether a computer vision model can use image properties (i.e., pixels alone) to replicate human inferences (Study 3). While our best model outperforms a single human rater, its absolute performance falls short of reliability conventions in psychological research. Finally, we introduce a large database of clothing images with psychological labels and demonstrate its use for exploration and replication of psychological research. The database consists of 5000 images of (western) women’s clothing items with psychological inferences annotated by 25 participants per clothing item.

Keywords: Computational social science; Computer vision; Neural networks; Digital aesthetics; Fashion (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s42001-020-00085-6

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