深度学习揭示了不同文化的人对面部表情的意义
Jeffrey A Brooks1,2, Lauren Kim1, Michael Opara1
1Research Division, Hume AI, New York, NY 10010, USA.
iScience
|March 4, 2024
概括
这项研究利用大规模数据和机器学习在六个国家绘制了面部表情的含义. 它发现了面部表情的21个普遍维度,揭示了人类共同的情感交流.
科学领域:
- 心理学 心理学 心理学
- 计算机科学 计算机科学
- 语言学的语言学.
背景情况:
- 之前关于面部表情的跨文化研究使用了有限的刺激和参与者数量.
- 了解面部表情意义的普遍性和文化特异性对于人类沟通研究至关重要.
研究的目的:
- 用大规模数据和先进的机器学习绘制跨文化面部表情的含义.
- 识别面部表情意义的普遍和文化特定的维度.
主要方法:
- 收集了来自六个国家的5833名参与者的大规模数据,从自然主义图像中产生了超过423,000个面部表情.
- 利用模仿范式,参与者复制了面部表情,并在48种情绪和心理状态中对它们进行了评分,这些情绪和心理状态都是用母语表达的.
- 采用深度神经网络来分析面部动作,预测文化特定的含义,并识别表情维度,同时控制外观和背景.
主要成果:
- 发现了面部表情意义的28个不同的维度.
- 识别了面部表情的21个维度,有强有力的文化跨越普遍性的证据.
- 发现剩余的维度表现出不同程度的文化特异性.
结论:
- 这项研究提供了一个前所未有的详细地图,表达面部表情的含义在文化内部和跨文化.
- 研究结果表明,人类面部表情的解释具有显著的普遍性,以及文化特异性的细微差别.
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