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Four dimensions characterize comprehensive trait judgments of faces

Lin, Chujun and Keleş, Ümit and Adolphs, Ralph (2019) Four dimensions characterize comprehensive trait judgments of faces. . (Unpublished) https://resolver.caltech.edu/CaltechAUTHORS:20200117-111031731

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Abstract

People readily attribute many traits to faces: some look beautiful, some competent, some aggressive. These snap judgments have important consequences in real life, ranging from success in political elections to decisions in courtroom sentencing. Modern psychological theories argue that the hundreds of different words people use to describe others from their faces are well captured by only two or three dimensions, such as valence and dominance, a highly influential framework that has been the basis for numerous studies in social and developmental psychology, social neuroscience, and in engineering applications. However, all prior work has used only a small number of words (12 to 18) to derive underlying dimensions, limiting conclusions to date. Here we employed deep neural networks to select a comprehensive set of 100 words that are representative of the trait words people use to describe faces, and to select a set of 100 faces. In two large-scale, preregistered studies we asked participants to rate the 100 faces on the 100 words (obtaining 2,850,000 ratings from 1,710 participants), and discovered a novel set of four psychological dimensions that best explain trait judgments of faces: warmth, competence, femininity, and youth. We reproduced these four dimensions across different regions around the world, in both aggregated and individual-level data. These results provide a new and most comprehensive characterization of face judgments, and reconcile prior work on face perception with work in social cognition and personality psychology.


Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription
https://doi.org/10.31234/osf.io/87nexDOIDiscussion Paper
https://osf.io/4mvytRelated ItemSupplementary Materials
http://bit.ly/osfface1Related ItemData/Code
http://bit.ly/osfface2Related ItemData/Code
ORCID:
AuthorORCID
Lin, Chujun0000-0002-7605-6508
Adolphs, Ralph0000-0002-8053-9692
Alternate Title:Four dimensions underlie face impressions, Comprehensive trait attributions show that face impressions are organized in four dimensions
Additional Information:License: CC-By Attribution 4.0 International. Created: October 01, 2019; Last edited: June 12, 2020. We thank Dean Mobbs, R. Michael Alvarez, Mark Bowren, Antonio Rangel, Clare Sutherland, Uri Maoz, and William Revelle for their input, Remya Nair and Christopher J. Birtja for technology support, and Becky Santora for helping with testing participants in foreign locations through Digital Divide Data. Funded in part by NSF grants BCS-1840756 and BCS-1845958, and the Carver Mead New Adventures Fund. Author Contributions: C.L. and R.A. developed the study concept and designed the study; C.L. and U.K. prepared experimental materials; R.A. supervised the experiments and analyses; C.L. performed and supervised data collection; C.L. and U.K. performed data analyses; C.L. and R.A. drafted the manuscript; all authors revised and reviewed the manuscript and approved the final manuscript for submission. The authors declare no competing interests. Data and code availability: All data, codes, and materials are available at Open Science Framework: https://osf.io/4mvyt/ and https://osf.io/xeb6w/.
Funders:
Funding AgencyGrant Number
NSFBCS-1840756
NSFBCS-1845958
Carver Mead New Adventures FundUNSPECIFIED
Subject Keywords:cross-culture; face perception; face recognition; factor analysis; first impressions; neural network; pre-registration; social cognition; text analysis; trait attributions
Record Number:CaltechAUTHORS:20200117-111031731
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20200117-111031731
Official Citation:Lin, C., Keles, U., & Adolphs, R. (2019, October 2). Four dimensions characterize comprehensive trait judgments of faces. https://doi.org/10.31234/osf.io/87nex
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:100792
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:17 Jan 2020 19:20
Last Modified:12 Jun 2020 17:21

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