Deep learning reveals what facial expressions mean to people in different cultures.

Cross-cultural studies of the meaning of facial expressions have largely focused on judgments of small sets of stereotypical images by small numbers of people. Here, we used large-scale data collection and machine learning to map what facial expressions convey in six countries. Using a mimicry paradigm, 5,833 participants formed facial expressions found in 4,659 naturalistic images, resulting in 423,193 participant-generated facial expressions. In their own language, participants also rated each expression in terms of 48 emotions and mental states. A deep neural network tasked with predicting the culture-specific meanings people attributed to facial movements while ignoring physical appearance and context discovered 28 distinct dimensions of facial expression, with 21 dimensions showing strong evidence of universality and the remainder showing varying degrees of cultural specificity. These results capture the underlying dimensions of the meanings of facial expressions within and across cultures in unprecedented detail.

Deep learning reveals what vocal bursts express in different cultures.

Human social life is rich with sighs, chuckles, shrieks and other emotional vocalizations, called 'vocal bursts'. Nevertheless, the meaning of vocal bursts across cultures is only beginning to be understood. Here, we combined large-scale experimental data collection with deep learning to reveal the shared and culture-specific meanings of vocal bursts. A total of n = 4,031 participants in China, India, South Africa, the USA and Venezuela mimicked vocal bursts drawn from 2,756 seed recordings. Participants also judged the emotional meaning of each vocal burst. A deep neural network tasked with predicting the culture-specific meanings people attributed to vocal bursts while disregarding context and speaker identity discovered 24 acoustic dimensions, or kinds, of vocal expression with distinct emotion-related meanings. The meanings attributed to these complex vocal modulations were 79% preserved across the five countries and three languages. These results reveal the underlying dimensions of human emotional vocalization in remarkable detail.