遗传算法揭示身份 情绪表达的独立表示 遗传算法揭示身份情绪表达的独立表示
Thomas Murray1, Nicola Binetti2, Christina Carlisi3
1Department of Psychology, University of Cambridge.
Emotion (Washington, D.C.)
|August 10, 2023
概括
对于大多数情绪来说,个体的情感识别信念是独一无二的,并且独立于身份. 然而,身份之间存在系统的,共同的面部表情差异.
科学领域:
- 认知心理学 认知心理学
- 社会心理学 社会心理学
- 神经科学是一个神经科学.
背景情况:
- 面部暗示情绪和身份被自动处理.
- 以前关于身份独立情绪编码的研究使用了姿势表达,忽视了个体差异.
研究的目的:
- 调查关于面部表情表达的信念是否依赖于身份.
- 探索个体在情绪识别和表达生成方面的差异.
主要方法:
- 利用基因算法让参与者创造出愤怒,恐惧,快乐和悲伤的面部表情.
- 通过权重操纵面部特征来分析表情空间.
- 应用机器学习来识别表达式中的系统差异.
主要成果:
- 参与者产生了独立于身份的愤怒,恐惧和快乐的面部表情,这些表情是个人的独特特征.
- 悲伤的表达并没有显示出这种身份的独立性.
- 机器学习揭示了两个身份之间的表达方式的系统,共享的差异.
结论:
- 关于面部表情表达的信念在很大程度上是个人的,并且独立于身份.
- 尽管个体的独特性,一些交叉身份表达模式是普遍认可的.
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