Tracking the affective state of unseen persons

Proceedings of the National Academy of Sciences of the United States of America
Zhimin Chen, David Whitney

Abstract

Emotion recognition is an essential human ability critical for social functioning. It is widely assumed that identifying facial expression is the key to this, and models of emotion recognition have mainly focused on facial and bodily features in static, unnatural conditions. We developed a method called affective tracking to reveal and quantify the enormous contribution of visual context to affect (valence and arousal) perception. When characters' faces and bodies were masked in silent videos, viewers inferred the affect of the invisible characters successfully and in high agreement based solely on visual context. We further show that the context is not only sufficient but also necessary to accurately perceive human affect over time, as it provides a substantial and unique contribution beyond the information available from face and body. Our method (which we have made publicly available) reveals that emotion recognition is, at its heart, an issue of context as much as it is about faces.

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Citations

Jul 18, 2019·Psychological Science in the Public Interest : a Journal of the American Psychological Society·Alan CowenDacher Keltner
Sep 10, 2020·Scientific Data·Mingming ZhangWenbo Luo
Feb 28, 2020·Nature·Douglas Heaven
Jun 13, 2020·Cognitive Research: Principles and Implications·Yavin Alwis, Jason M Haberman
Mar 23, 2019·Proceedings of the National Academy of Sciences of the United States of America·Aleix M Martinez

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