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Validation of data-driven computational models of social perception of faces.
Abstract
People rapidly form impressions from facial appearance, and these impressions affect social decisions. We argue that data-driven, computational models are the best available tools for identifying the source of such impressions. Here we validate seven computational models of social judgments of faces: attractiveness, competence, dominance, extroversion, likability, threat, and trustworthiness. The models manipulate both face shape and reflectance (i.e., cues such as pigmentation and skin smoothness). We show that human judgments track the models' predictions (Experiment 1) and that the models differentiate between different judgments, though this differentiation is constrained by the similarity of the models (Experiment 2). We also make the validated stimuli available for academic research: seven databases containing 25 identities manipulated in the respective model to take on seven different dimension values, ranging from -3 SD to +3 SD (175 stimuli in each database). Finally, we show how the computational models can be used to control for shared variance of the models. For example, even for highly correlated dimensions (e.g., dominance and threat), we can identify cues specific to each dimension and, consequently, generate faces that vary only on these cues.
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“A second limitation was that we only used male Caucasian faces. This decision was made in the light of evidence that there is a bias to classify bald, hairless faces as males (Todorov et al., 2013); thus, it is possible that our findings may not extend to females faces as well as faces from different ethnic backgrounds.”
From Mistrust and negative self‐esteem: Two paths from attachment styles to paranoia. (2021) · cites this paper as Todorov et al., 2013
In the citing paper
“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 [63]
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Albohn, D. N., Uddenberg, S., & Todorov, A. T. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.997498 · Cited by 12
Self-citationFace-Trait and Face-Race Cues in Adults' and Children's Social Evaluations
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Metadata
- Authors
- Todorov, A., Dotsch, R., Porter, J. M., Oosterhof, N. N., & Falvello, V. B.
- Affiliations
- Princeton University (US)
- Columbia University (US)
- University of Trento (IT)
- Venue
- Emotion
- Published
- 2013-08
- Volume
- 13
- Issue
- 4
- Pages
- 724-738
- ISSN
- 1528-3542 (ISSN-L)
- 1931-1516
- OpenAlex
- W2140051380
- Cited by
- 226
- References
- 55
- Citations by year
- 2026: 6 · 2025: 11 · 2024: 13 · 2023: 22 · 2022: 12 · 2021: 36 · 2020: 27 · 2019: 25 · 2018: 25 · 2017: 18 · 2016: 11 · 2015: 13 · 2014: 5 · 2013: 2
- FWCI
- 5.65
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Emotion (publishedVersion)
- PubMed (publishedVersion)
- Institutional Research Information System (Università degli Studi di Trento) (submittedVersion)
- Radboud Repository (Radboud University) (submittedVersion)
- 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 perception, computational model, model validation, facial attractiveness, perceived competence, perceived dominance, likeability, threat perception, perceived trustworthiness, facial shape, pigmentation, skin smoothness, face database
- MeSH terms
- Adult, Computer Simulation, Cues, Face, Facial Expression, Female, Humans, Male, Models, Psychological, Pattern Recognition, Visual, Social Perception, Databases, Factual, Young Adult
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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