Mapping the Cultural Landscape of Emotions for Social Interaction

随着全球化的快速发展，跨文化交流是现代社会不可或缺的一部分，对情感的相互理解是成功社交的关键。然而，一些文化误解了面部表情，挑战了被广泛接受的“情感通用语言”。我的研究旨在了解跨文化情感交流的复杂性。专注于面部表情的解码，我在《当代生物学》中表明，东亚（EA）群体误解面部表情是由于一种文化特定的解码策略，选择模棱两可的眼睛信息。反驳普遍性，我的工作引起了全世界的兴趣（探索频道杂志，国家地理，BBC新闻头版）。在《实验心理学杂志：总论》中，我展示了西高加索人（WC）和EA群体在心理上使用不同的表达特征来表达情感。在这些数据的基础上，我使用最先进的面部动画技术构建了每种文化中6种基本情绪面部表情的4-D心理模型。在这里，我展示了面部信号的特定文化模式（即，EA信号的情绪强度与早期眼睛活动），并且情绪并没有普遍地分为6个基本类别。这些数据否定了普遍性，强调了弥合知识差距的必要性。在获得资助期间，我的目标是通过以下一系列实验来弥合这些差距：1。情感的概念景观。这6种基本情绪并不是通用的，而是针对每个人的。使用分层语义网络重建工具，我将绘制情感词的概念组织，以揭示WC和EA文化中社会互动的主要情感类别。2. 特定文化的面部表情谱。使用上述情感术语和4-D面部动画平台，我将重建每种文化中与社会相关的面部表情光谱的4-D模型。使用已建立的分析工具，我将精确地识别出具有特定文化特征的面部表情的面部信号。跨文化情感识别。目前的面部表情刺激仅在WC文化中被识别，因此不能普遍地代表情感，限制了跨文化情感交流的认识。提供广泛的特定文化的4-D面部表情模型将极大地改善当前的刺激，包括有史以来第一套EA有效的面部表情。因此，我将对相同文化和其他文化的面部表情识别进行以前不可能的完全平衡的检查，使用眼动追踪来识别支持情感识别的面部信号，并在文化边界之间制造混乱。4. 内群体优势。4-D刺激可以灵活地将面部种族与文化情绪（例如，WC面部的EA情绪）交换。通过分离两种群体标识符（种族和文化情感）对情感识别的影响，我将解决群体内优势理论中的关键问题。应用和好处。我的数据将对科学和社会产生广泛的影响。提供跨文化情感交流的全面说明将弥合知识差距并重新点燃关键的社会心理学主题。改进的刺激将在行为和大脑水平上推进情绪处理的研究。更广泛地说，计算机科学（例如，人机交互）将从改进的人类情感模型中获得实际的好处，扩展到商业游戏行业和数字经济中发展的化身和伴侣机器人。全世界对我的研究的兴趣表明，社会高度重视跨文化互动。提供有关特定文化情感的建议将改善社会沟通，促进整个社会的健康关系。我的数据将为社会快速发展的通信需求做出及时的贡献，对英国的经济表现有很大的好处。

With rapid globalisation, cross-cultural communication is integral to modern society, with mutual understanding of emotions key to successful social interaction. Yet, some cultures misinterpret facial expressions, challenging the widely accepted 'universal language of emotion.' My research aims to understand the complexities of cross-cultural emotion communication.Focusing on the decoding of facial expressions, I showed in Current Biology that East Asian (EA) groups misinterpret facial expressions due to a culture-specific decoding strategy that selects ambiguous eye information. Refuting universality, my work elicited worldwide interest (Discovery Channel Magazine, National Geographic, BBC News Front Page). In Journal of Experimental Psychology: General, I then showed that Western Caucasian (WC) and EA groups mentally represent emotions using distinct expressive features. Building on these data further, I used state-of-the-art facial animation technology to construct 4-D mental models of the 6 basic facial expressions of emotions in each culture. Here, I show culture-specific patterns of facial signals (i.e., EA signal emotional intensity with early eye activity) and that emotion is not universally organized into 6 basic categories. These data refute universality, highlighting the need to bridge knowledge gaps. During the grant, I aim to bridge these gaps by conducting the following set of experiments: 1. Conceptual landscape of emotions. The 6 basic emotions are not universal, but WC-specific. Using hierarchical semantic network reconstruction tools, I will map the conceptual organisation of emotion words to reveal the main emotion categories central for social interaction in WC and EA culture. 2. Spectrum of culture-specific facial expressions. Using the emotion terms above and the 4-D facial animation platform, I will reconstruct 4-D models of a spectrum of socially relevant facial expressions in each culture. Using established analysis tools I will precisely identify the facial signals that characterise culture-specific facial expressions.3. Cross-cultural emotion recognition. Current facial expression stimuli are recognised only by WC cultures, so do not universally represent emotion, limiting knowledge of cross-cultural emotion communication. Providing a broad spectrum of culture-specific 4-D facial expression models will greatly improve current stimuli, including the first ever set of EA valid facial expressions. Thus, I will conduct a previously impossible fully balanced examination of same- and other-culture facial expression recognition, using eye-tracking to identify the facial signals supporting emotion recognition and creating confusion across cultural boundaries. 4. In-group advantage. The 4-D stimuli can flexibly interchange race of face with cultural emotions (e.g., EA emotion on a WC face). By isolating the effects of two group identifiers (race and cultural emotions) on emotion recognition, I will address key questions in the in-group advantage theory. Applications & Benefits. My data will have broad implications for science and society.Science. Providing a full account of cross-cultural emotion communication will bridge knowledge gaps and re-ignite key Social Psychology topics. Improved stimuli will advance research on emotion processing at the behavioural and brain level. More broadly, Computer Science (e.g., Human-Robot Interaction) will gain practical benefits from improved models of human emotion, extending to the commercial gaming industry and the digital economy in developing avatars and companion robots.Society. Worldwide interest in my research shows that society highly values cross-cultural interactions. Providing advice on culture-specific emotions will improve social communication, fostering healthy relations across society. My data will make a timely contribution to the rapidly evolving communication needs of society with great benefits for the economic performance of the UK.