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 generate trustworthiness evaluations for the facial action units (smile, eye brows, etc.) of European portraits in large historical databases. Our results show that trustworthiness in portraits increased over the period 1500–2000 paralleling the decline of interpersonal violence and the rise of democratic values observed in Western Europe. Further analyses suggest that this rise of trustworthiness displays is associated with increased living standards.

Quantifying how social trust evolved throughout history can help us understand the long-run dynamics of our societies. Here, the authors show an increase in displays of trustworthiness, using a face processing algorithm on early to modern European portraits.

Details

Title
Tracking historical changes in trustworthiness using machine learning analyses of facial cues in paintings
Author
Safra Lou 1   VIAFID ORCID Logo  ; Chevallier Coralie 2 ; Grèzes, Julie 2 ; Baumard Nicolas 3   VIAFID ORCID Logo 

 ENS, PSL, Research University, Laboratoire de Neurosciences Cognitives, Département d’études cognitives, Paris, France (GRID:grid.5607.4) (ISNI:0000000121105547); ENS, EHESS, PSL Research University, CNRS, Institut Jean Nicod, Département d’études cognitives, Paris, France (GRID:grid.4444.0) (ISNI:0000 0001 2112 9282); Sciences Po, CEVIPOF, CNRS, Paris, France (GRID:grid.4444.0) (ISNI:0000 0001 2112 9282) 
 ENS, PSL, Research University, Laboratoire de Neurosciences Cognitives, Département d’études cognitives, Paris, France (GRID:grid.5607.4) (ISNI:0000000121105547) 
 ENS, EHESS, PSL Research University, CNRS, Institut Jean Nicod, Département d’études cognitives, Paris, France (GRID:grid.4444.0) (ISNI:0000 0001 2112 9282) 
Publication year
2020
Publication date
2020
Publisher
Nature Publishing Group
e-ISSN
20411723
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2449449986
Copyright
© The Author(s) 2020. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.