Computational modelling of culture-specific social facial signals with transference to digital agents

战略优先领域-心理学关键词-社会感知，心理物理学，道歉面部表情是社交沟通的有力工具，因为它们可以传递无数信息，包括情绪（例如，杰克等人，2016）和人格特质（例如，Gill等人，2014）。一个长期的目标是了解面部交流系统，即，什么特定的面部运动向谁以及在什么文化中传递什么信息（例如，达尔文，1872/1999）。一个有影响力的理论认为，面部表情包括核心的跨文化信号加上文化特定的口音，阻碍跨文化沟通，同时赋予群体优势（例如，Elfenbein和Ambady，2002年）。另一种理论（例如，Tsai，2007）假设特定文化的理想面部表情会影响（例如，美国与中国-广泛与内容微笑）影响关键的社会判断（例如，智力，领导力; Park等人，2020; Krys等人，2014），这可能会加剧跨文化的误解。然而，理解动态面部信号的复杂相互作用及其对社会感知的影响在经验上仍然具有挑战性，因为面部是高度复杂的（例如，Jack & Schyns，2017）。我们将使用我们最先进的基于计算机图形的3D人脸生成器，严格的数据驱动方法和精确的信息理论分析工具（例如，杰克等人，2012; Liu等人，2021年; Zhan等人，2021）数学建模的动态面部线索（运动/形状/肤色），驱动关键的社会判断（情绪和社会特征）在不同的文化（东亚，西方）的个人。项目影响科学。该项目将提供一个新的，文化上细致入微的社会面部信号的帐户，直接影响中心辩论/理论，包括文化的普遍性/变异性，本体论，和面部表情的功能（例如，Elfenbein和Ambady，2002年; Jack等人，2016; Shariff & Tracy，2011）以及它们与面部身份线索（形状/肤色）在塑造社会感知（例如，Gill等人，2014）。社会这项工作与社会的几个方面直接相关，特别是随着全球化，文化融合和数字经济的发展。了解面部表情的跨文化相似性和差异可以直接影响日常的跨文化交流，以避免/减少误解，并为新的远程/虚拟技术提供信息。因此，该项目将直接引起公众的兴趣，并产生直接适用的结果。此外，数字代理现在嵌入到人类社会中，包括陪伴，医疗保健，教育和娱乐，其中社交技能至关重要（Moclane，2015）。通过将文化和社会上细微差别的面部信号传输给数字代理，该项目旨在增强他们的社会沟通能力，实用性，可访问性和全球市场。培训计划Year 1.专业技能的内部培训（例如，高级编程：Matlab/Python; 3D/4D面部信号建模;专家工具箱），文献综述，以发展知识，提交伦理学申请，收集试点数据。学生将被要求提供直接投入，在塑造项目的方向，利用他们获得的文献知识，并参加1-2-1和小组实验室会议，讨论和审查这些研究方向。第二年。继续收集数据，建立跨文化的面部信号的计算模型，进行初步分析，准备提交给国际会议的早期工作，以获得反馈。完成数据收集和分析，准备向同行评审的广泛受众期刊提交稿件;将面部信号传输给数字代理，评估影响，并准备向同行评审的社交机器人期刊/会议提交更多稿件。

Strategic priority area-PsychologyKeywords-social perception, psychophysics, apologyFacial expressions are a powerful tool for social communication because they can transmit myriad information, including emotions (e.g., Jack et al., 2016) and personality traits (e.g., Gill et al., 2014). A longstanding goal is to understand the system of facial communication-i.e., what specific facial movements transmit what messages to whom and in what culture (e.g., Darwin, 1872/1999). One influential theory posits that facial expressions comprise core cross-cultural signals plus culture-specific accents that hinder cross-cultural communication while conferring in-group advantages (e.g., Elfenbein & Ambady, 2002). Relatedly, another theory (e.g., Tsai, 2007) posits that facial expressions of culture-specific ideal affect (e.g., USA vs. China-broad vs. content smiles) influence key social judgments (e.g., intelligence, leadership; Park et al., 2020; Krys et al., 2014) that could compound cross-cultural miscommunication. Yet, understanding the complex interplay of dynamic facial signals and their impact on social perception remains empirically challenging because the face is highly complex (e.g., Jack & Schyns, 2017). We will address this empirical challenge using our state-of-the-art computer graphics-based 3D face generator, rigorous data-driven methods, and precise information-theoretic analyses tools (e.g., Jack et al., 2012; Liu et al., 2021; Zhan et al., 2021) to mathematically model the dynamic facial cues (movements/shape/complexion) that drive key social judgments (emotion and social traits) in individuals in distinct cultures (East Asian, Western). Project impactScience. This project will provide a new, culturally nuanced account of social facial signalling with direct impact in central debates/theories, including the cultural universality/variability, ontology, and function of facial expressions (e.g., Elfenbein & Ambady, 2002; Jack et al., 2016; Shariff & Tracy, 2011) and their intersection with face identity cues (shape/complexion) in shaping social perception (e.g., Gill et al., 2014). Society. This work is directly relevant to several facets of society, particularly with increasing globalisation, cultural integration, and the digital economy. Understanding cross-cultural similarities and differences in facial expressions can directly impact everyday cross-cultural communications to avoid/reduce misunderstandings and inform new remote/virtual technologies. Therefore, the project will be of immediate interest to the public with directly applicable results. Further, digital agents are now embedded into human society including for companionship, health care, education, and entertainment, where social skills are vital (Morency, 2015). By transferring the culturally and socially nuanced facial signals to digital agents, this project aims to enhance their social communication capabilities, utility, accessibility, and global marketability. Training planYear 1. In-house training in specialist skills (e.g., advanced programming: Matlab/Python; 3D/4D face signal modelling; specialist toolboxes), literature reviews to develop knowledge, submit ethics applications, collect pilot data. The student will be expected to provide direct inputs in shaping the direction of the project using their acquired knowledge of the literature, and to participate in 1-2-1 and group laboratory meetings to discuss and review these research directions. Year 2. Continue data collection, build computational models of facial signals across cultures, conduct primary analysis, prepare early work for submissions to international conferences to obtain feedback.Year 3-3.5. Finalise data collection and analysis, prepare submission of manuscripts to peer- reviewed broad-audience journals; transfer facial signals to digital agents, evaluate impact, and prepare additional submission of manuscripts to peer-reviewed social robotics journals/conferences.