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Emotion is perceived accurately from isolated body parts, especially hands

Blythe, E., Garrido, L. ORCID: 0000-0002-1955-6506 & Longo, M. R. (2023). Emotion is perceived accurately from isolated body parts, especially hands. Cognition, 230, article number 105260. doi: 10.1016/j.cognition.2022.105260

Abstract

Body posture and configuration provide important visual cues about the emotion states of other people. We know that bodily form is processed holistically, however, emotion recognition may depend on different mechanisms; certain body parts, such as the hands, may be especially important for perceiving emotion. This study therefore compared participants' emotion recognition performance when shown images of full bodies, or of isolated hands, arms, heads and torsos. Across three experiments, emotion recognition accuracy was above chance for all body parts. While emotions were recognized most accurately from full bodies, recognition performance from the hands was more accurate than for other body parts. Representational similarity analysis further showed that the pattern of errors for the hands was related to that for full bodies. Performance was reduced when stimuli were inverted, showing a clear body inversion effect. The high performance for hands was not due only to the fact that there are two hands, as performance remained well above chance even when just one hand was shown. These results demonstrate that emotions can be decoded from body parts. Furthermore, certain features, such as the hands, are more important to emotion perception than others.

Publication Type: Article
Additional Information: © 2022. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/
Publisher Keywords: Emotion recognition, Hands, Bodies, Body perception
Subjects: B Philosophy. Psychology. Religion > BF Psychology
Departments: School of Health & Psychological Sciences > Psychology
SWORD Depositor:
[thumbnail of 2022_Blythe_et_al_2023_Cognition_revised.docx] Text - Accepted Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.

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