Journal Article

Sixteen facial expressions occur in similar contexts worldwide

Cowen, Alan S., Prasad, Gautam, Keltner, Dacher, Adam, Hartwig, Schroff, Florian, Jou, Brendan

2021

Overview

Understanding the degree to which human facial expressions co-vary with specific social contexts across cultures is central to the theory that emotions enable adaptive responses to important challenges and opportunities 1 – 6 . Concrete evidence linking social context to specific facial expressions is sparse and is largely based on survey-based approaches, which are often constrained by language and small sample sizes 7 – 13 . Here, by applying machine-learning methods to real-world, dynamic behaviour, we ascertain whether naturalistic social contexts (for example, weddings or sporting competitions) are associated with specific facial expressions 14 across different cultures. In two experiments using deep neural networks, we examined the extent to which 16 types of facial expression occurred systematically in thousands of contexts in 6 million videos from 144 countries. We found that each kind of facial expression had distinct associations with a set of contexts that were 70% preserved across 12 world regions. Consistent with these associations, regions varied in how frequently different facial expressions were produced as a function of which contexts were most salient. Our results reveal fine-grained patterns in human facial expressions that are preserved across the modern world. An analysis of 16 types of facial expression in thousands of contexts in millions of videos revealed fine-grained patterns in human facial expression that are preserved across the modern world.

• Publisher: Nature Publishing Group UK,Nature Publishing Group
• Subject: 631/378/1457 / 631/477/2811 / Analysis / Annotations / Artificial neural networks / Behavior / Ceremonial Behavior / Context effects (Psychology) / Culture / Deep Learning / Emotions / Experiments / Facial Expression / Geographic Mapping / Humanities and Social Sciences / Humans / Internationality / Learning algorithms / Machine learning / Martial arts / multidisciplinary / Neural networks / Object recognition (Computers) / Pain / Pattern recognition / Popular Culture / Regions / Science / Science (multidisciplinary) / Technology application / Translations / Varieties / Weightlifting