Brooks Jeffrey A, Kim Lauren, Opara Michael, Keltner Dacher, Fang Xia, Monroy Maria, Corona Rebecca, Tzirakis Panagiotis, Baird Alice, Metrick Jacob, Taddesse Nolawi, Zegeye Kiflom, Cowen Alan S
Research Division, Hume AI, New York, NY 10010, USA.
Department of Psychology, University of California, Berkeley, Berkeley, CA 94720, USA.
iScience. 2024 Feb 10;27(3):109175. doi: 10.1016/j.isci.2024.109175. eCollection 2024 Mar 15.
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.
对面部表情含义的跨文化研究主要集中在少数人对一小部分刻板图像的判断上。在此，我们运用大规模数据收集和机器学习来描绘六个国家中面部表情所传达的信息。采用模仿范式，5833名参与者做出了在4659张自然主义图像中出现的面部表情，从而产生了423193个由参与者生成的面部表情。参与者还用自己的语言根据48种情绪和心理状态对每个表情进行了评分。一个深度神经网络负责预测人们赋予面部动作的特定文化含义，同时忽略外貌和背景，它发现了28个不同的面部表情维度，其中21个维度有强有力的普遍性证据，其余维度则表现出不同程度的文化特异性。这些结果以前所未有的细节揭示了不同文化内部和跨文化的面部表情含义的潜在维度。
