The functional basis of face evaluation
Open accessAbstract
People automatically evaluate faces on multiple trait dimensions, and these evaluations predict important social outcomes, ranging from electoral success to sentencing decisions. Based on behavioral studies and computer modeling, we develop a 2D model of face evaluation. First, using a principal components analysis of trait judgments of emotionally neutral faces, we identify two orthogonal dimensions, valence and dominance, that are sufficient to describe face evaluation and show that these dimensions can be approximated by judgments of trustworthiness and dominance. Second, using a data-driven statistical model for face representation, we build and validate models for representing face trustworthiness and face dominance. Third, using these models, we show that, whereas valence evaluation is more sensitive to features resembling expressions signaling whether the person should be avoided or approached, dominance evaluation is more sensitive to features signaling physical strength/weakness. Fourth, we show that important social judgments, such as threat, can be reproduced as a function of the two orthogonal dimensions of valence and dominance. The findings suggest that face evaluation involves an overgeneralization of adaptive mechanisms for inferring harmful intentions and the ability to cause harm and can account for rapid, yet not necessarily accurate, judgments from faces.
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Trustworthiness judgements are a dominant mode of appraisal when encountering unfamiliar faces.
“Trustworthiness judgements are a dominant mode of appraisal when encountering unfamiliar faces (Oosterhof & Todorov, 2008).”
From Mistrust and negative self‐esteem: Two paths from attachment styles to paranoia. (2021) · cites this paper as Oosterhof & Todorov, 2008
The choice of using intelligence, trustworthiness, sociability and happiness as dependent attributes for the halo effect was driven by existing literature.
“The choice of using intelligence, trustworthiness, sociability and happiness as dependent attributes for the halo effect was driven by existing literature on this cognitive bias, such as [4,6,8,9,22,53,60–63].”
From What is beautiful is still good: the attractiveness halo effect in the era of beauty filters (2024) · cites this paper as [60]
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- Authors
- Oosterhof, N. N., & Todorov, A.
- Affiliations
- Princeton University (US)
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2008-08-12
- Volume
- 105
- Issue
- 32
- Pages
- 11087-11092
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC2516255
- OpenAlex
- W1971687303
- Cited by
- 1,819
- References
- 8
- Citations by year
- 2026: 86 · 2025: 107 · 2024: 138 · 2023: 144 · 2022: 140 · 2021: 178 · 2020: 156 · 2019: 137 · 2018: 125 · 2017: 114 · 2016: 101 · 2015: 87 · 2014: 76 · 2013: 71 · 2012: 55
- FWCI
- 17.80
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Science Foundation (0446846)
- Netherlands Organization for International Cooperation in Higher Education
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Face Recognition and Perception
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Evolutionary Psychology and Human Behavior
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Keywords
- face evaluation, facial expression perception, valence, dominance, trustworthiness, threat perception, social judgment, facial features
- MeSH terms
- Computer Simulation, Emotions, Face, Humans, Image Processing, Computer-Assisted
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