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Using portraits to quantify the changes of generalized social trust in European history: A replication study

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  • Léonard Guillou
  • Lou Safra
  • Nicolas Baumard

Abstract

A portrait is an exercise of impression management: the sitter can choose the impression she or he wants to create in the eyes of others’: competence, trustworthiness, dominance, etc. Indirectly, this choice informs us about the qualities that were specifically valued at the time the portrait was created. In a previous paper, we have shown that cues of perceived trustworthiness in portraits increased in time during the modern period in Europe, meaning that people probably granted more importance to be seen as a trustworthy person. Moreover, this increase is correlated to economic development. In this study, we aim to replicate this result, using more controlled databases: 1) a newly created database of European head-of-state sovereigns (N = 966, from 1400 to 2020), that is a database of individuals holding the same social position across time and countries, and 2) a database of very high-quality portraits digitized with the same technique, and coming from the same Museum, the Chateau de Versailles database (N = 2,291, from 1483 to 1938). Using mixed effects linear models, we observed in the first dataset that the modeled perceived facial trustworthiness of these sovereigns’ faces increased over time (b = 0.182 ± 0.04 s.e.m., t(201) = 4.40, p .250). We conclude by discussing the potential of this new technique to uncover long-term behavioral changes in history, as well as its limitations.

Suggested Citation

  • Léonard Guillou & Lou Safra & Nicolas Baumard, 2023. "Using portraits to quantify the changes of generalized social trust in European history: A replication study," PLOS ONE, Public Library of Science, vol. 18(9), pages 1-14, September.
  • Handle: RePEc:plo:pone00:0289741
    DOI: 10.1371/journal.pone.0289741
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    3. Lou Safra & Coralie Chevallier & Julie Grèzes & Nicolas Baumard, 2020. "Tracking historical changes in perceived trustworthiness in Western Europe using machine learning analyses of facial cues in paintings," Nature Communications, Nature, vol. 11(1), pages 1-7, December.
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