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Statistical learning shapes face evaluation

Nature Human Behaviour volume 1, Article number: 0001 (2017) Cite this article

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Abstract

The belief in physiognomy—the art of reading character from faces—has been with us for centuries1–3. People everywhere infer traits (for example, trustworthiness) from faces, and these inferences predict economic, legal and even voting decisions2,4. Research has identified many configurations of facial features that predict specific trait inferences2,5–14, and detailed computational models of such inferences have recently been developed5–7,15–17. However, these configurations do not fully account for trait inferences from faces. Here, we propose a new direction in the study of inferences from faces, inspired by a cognitive–ecological18–20 and implicit-learning approach21,22. Any face can be positioned in a statistical distribution of faces extracted from the environment. We argue that understanding inferences from faces requires consideration of the statistical position of the faces in this learned distribution. Four experiments show that the mere statistical position of faces imbues them with social meaning: faces are evaluated more negatively the more they deviate from a learned central tendency. Our findings open new possibilities for the study of face evaluation, providing a potential model for explaining both individual and cross-cultural variation, as individuals are immersed in varying environments that contain different distributions of facial features.

To examine the hypothesis that the location of a face on a statistical distribution of facial features is used for evaluative inferences, we exposed participants to faces drawn from different distributions that vary in a range of statistical properties (such as the central tendency, dispersion, shape) and asked them to judge novel test faces. Crucially, because the test faces were identical in all conditions, any differences in the judgments between conditions can only be explained by the statistical properties of the faces. To generate the faces, we used a statistical face space model that captures the variance from a large sample of real faces with 130 orthogonal dimensions. Each dimension codes for different feature variance, and its magnitude corresponds to one standard deviation (s.d.) of the same feature variance in the sample of real faces. For instance, one dimension may code primarily for face width, and a face located at 1 s.d. on that dimension corresponds to a face width of 1 s.d. above the mean in the original sample. Thus, any face can be represented as a coordinate in the space, and any coordinate can be visualized as an image. The faces for our experiments always varied on a randomly generated dimension (the target dimension, see Methods and Supplementary Fig. 1) in the face space, with the constraint that it was orthogonal to known dimensions that are correlated with social judgments such as trustworthiness and dominance. Learning -phase stimuli additionally varied randomly in directions orthogonal to this target dimension (see Methods).

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Figure 1: Judgments study 1.
Figure 2: Judgments study 2.
Figure 3: Judgments study 3.
Figure 4: Judgments study 4.

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Acknowledgements

The authors are grateful to V. Falvello for her help in data collection, to A. Sklar for early discussions about the work, and to H. Aarts for commenting on a previous version of the manuscript. This research was supported by NWO Rubicon grant no. 446-10-014 awarded to R.D. and United States–Israel Binational Science Foundation grant no. 2013417 awarded to R.R.H. and A.T. The funders had no role in the study design, the data collection and analysis, the decision to publish or the preparation of the manuscript.

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Authors and Affiliations

  1. Department of Psychology, Utrecht University, PO Box 80.149, Utrecht, TC, 3508, The Netherlands

    Ron Dotsch

  2. Behavioural Science Institute, Radboud University, PO Box 9104, 6500, HE, Nijmegen, The Netherlands

    Ron Dotsch

  3. Department of Psychology, Hebrew University, Mt. Scopus, Jerusalem 91905, Israel

    Ran R. Hassin

  4. Department of Psychology, Princeton University, Peretsman-Scully Hall, Princeton, New Jersey 08540 USA

    Alexander Todorov

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  1. Ron Dotsch
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  3. Alexander Todorov
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Contributions

R.D. programmed the studies, analysed data and wrote the manuscript. All authors were involved in study design, discussed the results and edited the manuscript.

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Correspondence to Ron Dotsch.

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The authors declare no competing interests.

Supplementary information

Supplementary information

Supplementary Figures 1–7, Supplementary Methods and Supplementary Results (PDF 507 kb)

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Dotsch, R., Hassin, R. & Todorov, A. Statistical learning shapes face evaluation. Nat Hum Behav 1, 0001 (2017). https://doi.org/10.1038/s41562-016-0001

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