Abstract：
Video recordings accurately capture facial expression movements; however, they are difficult for face perception researchers to standardise and manipulate. For this reason, dynamic morphs of photographs are often used, despite their lack of naturalistic facial motion. This study aimed to investigate how humans perceive emotions from faces using real videos and two different approaches to artificially generating dynamic expressions - dynamic morphs, and AI-synthesised deepfakes. Our participants perceived dynamic morphed expressions as less intense when compared with videos (all emotions) and deepfakes (fearful, happy, sad). Videos and deepfakes were perceived similarly. Additionally, they perceived morphed happiness and sadness, but not morphed anger or fear, as less genuine than other formats. Our findings support previous research indicating that social responses to morphed emotions are not representative of those to video recordings. The findings also suggest that deepfakes may offer a more suitable standardized stimulus type compared to morphs. Additionally, qualitative data were collected from participants and analysed using ChatGPT, a large language model. ChatGPT successfully identified themes in the data consistent with those identified by an independent human researcher. According to this analysis, our participants perceived dynamic morphs as less natural compared with videos and deepfakes. That participants perceived deepfakes and videos similarly suggests that deepfakes effectively replicate natural facial movements, making them a promising alternative for face perception research. The study contributes to the growing body of research exploring the usefulness of generative artificial intelligence for advancing the study of human perception.
摘要：
视频记录准确地捕捉面部表情运动；然而，对于面部感知研究人员来说，它们很难标准化和操纵。出于这个原因,照片的动态变形经常被使用，尽管他们缺乏自然的面部运动。这项研究旨在研究人类如何使用真实视频和两种不同的方法来人工生成动态表情-动态变形，从面部感知情绪。和AI合成的深度假货。与视频（所有情绪）和深度假货（恐惧，快乐,sad).视频和deepfakes被认为是类似的。此外,他们感觉到了快乐和悲伤的变形，但没有变形的愤怒或恐惧，不如其他格式真实。我们的发现支持先前的研究，表明对变形情绪的社会反应并不代表视频记录。研究结果还表明，与变体相比，深度假货可能提供更合适的标准化刺激类型。此外,从参与者那里收集定性数据，并使用ChatGPT进行分析，一个大的语言模型。ChatGPT成功地在数据中确定了与独立人类研究人员确定的主题一致的主题。根据这一分析,我们的参与者认为动态变形与视频和深度假货相比不那么自然。参与者认为深度假货和视频类似地表明，深度假货有效地复制了自然的面部运动，使它们成为面部感知研究的有希望的替代品。这项研究有助于越来越多的研究探索生成人工智能对推进人类感知研究的有用性。
