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运用机器学习分析绘画中的面部线索来追踪可信赖性的历史变化。

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

机构信息

Laboratoire de Neurosciences Cognitives, Département d'études cognitives, ENS, PSL, Research University, INSERM, Paris, France.

Institut Jean Nicod, Département d'études cognitives, ENS, EHESS, PSL Research University, CNRS, Paris, France.

出版信息

Nat Commun. 2020 Sep 22;11(1):4728. doi: 10.1038/s41467-020-18566-7.

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.

摘要

社会信任与许多积极的社会成果相关联,包括提高经济绩效、降低犯罪率和更具包容性的制度。然而,信任的起源仍然难以捉摸,部分原因是社会信任很难及时记录下来。基于社会认知的最新进展,我们设计了一种算法,可以自动为大型历史数据库中欧洲肖像的面部动作单元(微笑、眉毛等)生成可信度评估。我们的研究结果表明,1500 年至 2000 年间,肖像的可信度随着西欧人际暴力的减少和民主价值观的兴起而增加。进一步的分析表明,这种可信度的提高与生活水平的提高有关。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6176/7508927/7efe7208961b/41467_2020_18566_Fig1_HTML.jpg

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