Department of Psychology, Fudan University, Handan Road 220, Shanghai, 200433, SH, People's Republic of China.
Department of Psychology, University of York, York, UK.
Sci Rep. 2023 Oct 3;13(1):16649. doi: 10.1038/s41598-023-43716-4.
Research on deception detection has mainly focused on Simple Deception, in which false information is presented as true. Relatively few studies have examined Sophisticated Deception, in which true information is presented as false. Because Sophisticated Deception incentivizes the appearance of dishonesty, it provides a window onto stereotypical beliefs about cues to deception. Here, we adapted the popular Joker Game to elicit spontaneous facial expressions under Simple Deception, Sophisticated Deception, and Plain Truth conditions, comparing facial behaviors in static, dynamic nonspeaking, and dynamic speaking presentations. Facial behaviors were analysed via machine learning using the Facial Action Coding System. Facial activations were more intense and longer lasting in the Sophisticated Deception condition than in the Simple Deception and Plain Truth conditions. More facial action units intensified in the static condition than in the dynamic speaking condition. Simple Deception involved leaked facial behaviors of which deceivers were unaware. In contrast, Sophisticated Deception involved deliberately leaked facial cues, including stereotypical cues to lying (e.g., gaze aversion). These stereotypes were inaccurate in the sense that they diverged from cues in the Simple Deception condition-the actual appearance of deception in this task. Our findings show that different modes of deception can be distinguished via facial action analysis. They also show that stereotypical beliefs concerning cues to deception can inform behavior. To facilitate future research on these topics, the multimodal stimuli developed in this study are available free for scientific use.
欺骗检测研究主要集中在简单欺骗上,即虚假信息被呈现为真实信息。相对较少的研究考察了复杂欺骗,即真实信息被呈现为虚假信息。由于复杂欺骗激励了不诚实的表现,它为关于欺骗线索的典型信念提供了一个窗口。在这里,我们改编了流行的小丑游戏,在简单欺骗、复杂欺骗和真实陈述条件下引出自发的面部表情,比较了静态、非言语动态和言语动态呈现下的面部行为。使用面部动作编码系统对面部行为进行了机器学习分析。与简单欺骗和真实陈述条件相比,复杂欺骗条件下的面部激活更强烈、持续时间更长。与动态说话条件相比,静态条件下更多的面部动作单元得到强化。简单欺骗涉及到欺骗者没有意识到的泄露的面部行为。相比之下,复杂欺骗涉及到故意泄露的面部线索,包括说谎的典型线索(例如,目光回避)。这些刻板印象是不准确的,因为它们与简单欺骗条件下的线索不同——在这个任务中,欺骗的实际表现。我们的研究结果表明,不同的欺骗模式可以通过面部动作分析来区分。它们还表明,关于欺骗线索的典型信念可以指导行为。为了促进这些主题的未来研究,本研究开发的多模态刺激可免费供科学使用。