Tracking historical changes in perceived trustworthiness in Western Europe using machine learning analyses of facial cues in paintings

Lou Safra (), Coralie Chevallier, Julie Grèzes and Nicolas Baumard ()
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Lou Safra: ENS, PSL, Research University
Coralie Chevallier: ENS, PSL, Research University
Julie Grèzes: ENS, PSL, Research University
Nicolas Baumard: ENS, EHESS, PSL Research University, CNRS

Nature Communications, 2020, vol. 11, issue 1, 1-7

Abstract: Abstract Social trust is linked to a host of positive societal outcomes, including improved economic performance, lower crime rates and more inclusive institutions. Yet, the origins of trust remain elusive, partly because social trust is difficult to document in time. Building on recent advances in social cognition, we design an algorithm to automatically estimate ratings of perceived trustworthiness evaluations from specific facial cues (such as muscle contractions associated with smiling) detected in European portraits in large historical databases. We used this measure as a proxy of social trust in history. Our results show that estimated levels of perceived trustworthiness in portraits increased over the period 1500–2000. Further analyses suggest that this rise of perceived trustworthiness is associated with increased living standards.

Date: 2020
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DOI: 10.1038/s41467-020-18566-7

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