Zheng Sarah Ying, Rozenkrantz Liron, Sharot Tali
Department of Security & Crime Sciences, Faculty of Engineering, University College London, London, UK.
Dawes Centre for Future Crime, University College London, London, UK.
Commun Psychol. 2024 Mar 14;2(1):21. doi: 10.1038/s44271-024-00068-7.
The surge of online scams is taking a considerable financial and emotional toll. This is partially because humans are poor at detecting lies. In a series of three online experiments (N = 102, N = 108, N = 100) where participants are given the opportunity to lie as well as to assess the potential lies of others, we show that poor lie detection is related to the suboptimal computations people engage in when assessing lies. Participants used their own lying behaviour to predict whether other people lied, despite this cue being uninformative, while under-using more predictive statistical cues. This was observed by comparing the weights participants assigned to different cues, to those of a model trained on the ground truth. Moreover, across individuals, reliance on statistical cues was associated with better discernment, while reliance on one's own behaviour was not. These findings suggest scam detection may be improved by using tools that augment relevant statistical cues.
网络诈骗的激增正在造成巨大的经济和情感损失。部分原因在于人类不擅长察觉谎言。在三项在线实验（样本量分别为N = 102、N = 108、N = 100）中，参与者有机会说谎并评估他人可能的谎言，我们发现，难以察觉谎言与人们在评估谎言时进行的次优计算有关。参与者利用自己的说谎行为来预测他人是否说谎，尽管这一线索并无信息价值，而对更具预测性的统计线索却利用不足。通过比较参与者赋予不同线索的权重与基于真实情况训练的模型的权重，我们观察到了这一点。此外，在个体层面，依赖统计线索与更强的辨别力相关，而依赖自身行为则不然。这些发现表明，使用增强相关统计线索的工具可能会提高诈骗检测能力。
