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Edited by Susan T. Fiske, Princeton University, Princeton, NJ, and approved July 7, 2014 (received for review May 27, 2014)
Significance
Understanding how first impressions are formed to faces is a topic of major theoretical and practical interest that has been given added importance through the widespread use of images of faces in social media. We create a quantitative model that can predict first impressions of previously unseen ambient images of faces (photographs reflecting the variability encountered in everyday life) from a linear combination of facial attributes, explaining 58% of the variance in raters’ impressions despite the considerable variability of the photographs. Reversing this process, we then demonstrate that face-like images can be generated that yield predictable social trait impressions in naive raters because they capture key aspects of the systematic variation in the relevant physical features of real faces.
Abstract
First impressions of social traits, such as trustworthiness or dominance, are reliably perceived in faces, and despite their questionable validity they can have considerable real-world consequences. We sought to uncover the information driving such judgments, using an attribute-based approach. Attributes (physical facial features) were objectively measured from feature positions and colors in a database of highly variable “ambient” face photographs, and then used as input for a neural network to model factor dimensions (approachability, youthful-attractiveness, and dominance) thought to underlie social attributions. A linear model based on this approach was able to account for 58% of the variance in raters’ impressions of previously unseen faces, and factor-attribute correlations could be used to rank attributes by their importance to each factor. Reversing this process, neural networks were then used to predict facial attributes and corresponding image properties from specific combinations of factor scores. In this way, the factors driving social trait impressions could be visualized as a series of computer-generated cartoon face-like images, depicting how attributes change along each dimension. This study shows that despite enormous variation in ambient images of faces, a substantial proportion of the variance in first impressions can be accounted for through linear changes in objectively defined features.
Footnotes
- ↵1To whom correspondence should be addressed. Email: tom.hartley{at}york.ac.uk.
Author contributions: A.W.Y. and T.H. designed research; R.J.W.V. and C.A.M.S. performed research; C.A.M.S. contributed new reagents/analytic tools; R.J.W.V. and C.A.M.S. analyzed data; R.J.W.V., C.A.M.S., A.W.Y., and T.H. wrote the paper; R.J.W.V. contributed to the development of the model and face coding scheme.; C.A.M.S. contributed to the design of the validation experiment.; A.W.Y. conceived the study; and T.H. conceived the study and contributed to modeling methods.
The authors declare no conflict of interest.
This article is a PNAS Direct Submission.
This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1409860111/-/DCSupplemental.
Freely available online through the PNAS open access option.
Modeling first impressions from facial images
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