Mapping the Cultural Landscape of Emotions for Social Interaction

点击翻译按钮获取中文摘要

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.