Four Not Six: Revealing Culturally Common Facial Expressions of Emotion
Four Not Six: Revealing Culturally Common Facial Expressions of Emotion
复制标题
DOI：
10.1037/xge0000162
复制
发表时间：
2016-06-01
影响因子：
4.1
通讯作者：
Schyns, Philippe G.
中科院分区：
文献类型：
--
作者：
Jack, Rachael E.;Sun, Wei;Schyns, Philippe G.
作为一个高度社会化的物种，人类产生复杂的面部表情来传达各种各样的情绪。自达尔文的工作以来，在这些复杂的模式中识别出哪些是跨文化的共同模式，哪些是特定文化的模式，一直是心理学、人类学、哲学以及最近的机器视觉和社会机器人学的中心问题。解决这个问题的经典方法通常测试了代表6种情绪的理论动机面部表情的跨文化识别，并报告了普遍性。然而，跨文化的可变识别准确性表明，更复杂的面部表情中嵌入的一组简单的表达模式支持更窄的跨文化交流。我们通过模拟两种文化中60多种情绪的面部表情来探索这一假设，并分离出潜在的表达模式。使用一个多学科的方法，我们首先映射的概念组织的一个广泛的情感词通过建立语义网络在2种文化。对于每种文化中的每个情感词，我们然后建模和验证其相应的动态面部表情，产生超过60个文化有效的面部表情模型。然后，我们应用到合并模型的多变量数据减少技术，揭示了4个潜在的和文化上常见的面部表情模式，每个通信的特定组合的效价，唤醒，和优势。然后，我们揭示了面部运动，强调每个潜在的表达模式，创造复杂的面部表情。我们的数据质疑了广泛持有的观点，即6种面部表情模式是普遍的，而不是暗示4种潜在的表达模式，直接影响情感交流，社会心理学，认知神经科学和社会机器人。
As a highly social species, humans generate complex facial expressions to communicate a diverse range of emotions. Since Darwin's work, identifying among these complex patterns which are common across cultures and which are culture-specific has remained a central question in psychology, anthropology, philosophy, and more recently machine vision and social robotics. Classic approaches to addressing this question typically tested the cross-cultural recognition of theoretically motivated facial expressions representing 6 emotions, and reported universality. Yet, variable recognition accuracy across cultures suggests a narrower cross-cultural communication supported by sets of simpler expressive patterns embedded in more complex facial expressions. We explore this hypothesis by modeling the facial expressions of over 60 emotions across 2 cultures, and segregating out the latent expressive patterns. Using a multidisciplinary approach, we first map the conceptual organization of a broad spectrum of emotion words by building semantic networks in 2 cultures. For each emotion word in each culture, we then model and validate its corresponding dynamic facial expression, producing over 60 culturally valid facial expression models. We then apply to the pooled models a multivariate data reduction technique, revealing 4 latent and culturally common facial expression patterns that each communicates specific combinations of valence, arousal, and dominance. We then reveal the face movements that accentuate each latent expressive pattern to create complex facial expressions. Our data questions the widely held view that 6 facial expression patterns are universal, instead suggesting 4 latent expressive patterns with direct implications for emotion communication, social psychology, cognitive neuroscience, and social robotics.