Published in

Nature Research, Nature Communications, 1(11), 2020

DOI: 10.1038/s41467-020-18566-7

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

Journal article published in 2020 by Lou Safra ORCID, Coralie Chevallier, Julie Grèzes, Nicolas Baumard ORCID
This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

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Abstract

AbstractSocial 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.