Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel.
Coller School of Management, Tel Aviv University, Tel Aviv, Israel.
Brain Behav. 2021 Dec;11(12):e2386. doi: 10.1002/brb3.2386. Epub 2021 Oct 22.
Deception is present in all walks of life, from social interactions to matters of homeland security. Nevertheless, reliable indicators of deceptive behavior in real-life scenarios remain elusive.
By integrating electrophysiological and communicative approaches, we demonstrate a new and objective detection approach to identify participant-specific indicators of deceptive behavior in an interactive scenario of a two-person deception task. We recorded participants' facial muscle activity using novel dry screen-printed electrode arrays and applied machine-learning algorithms to identify lies based on brief facial responses.
With an average accuracy of 73%, we identified two groups of participants: Those who revealed their lies by activating their cheek muscles and those who activated their eyebrows. We found that the participants lied more often with time, with some switching their telltale muscle groups. Moreover, while the automated classifier, reported here, outperformed untrained human detectors, their performance was correlated, suggesting reliance on shared features.
Our findings demonstrate the feasibility of using wearable electrode arrays in detecting human lies in a social setting and set the stage for future research on individual differences in deception expression.
欺骗存在于生活的方方面面，从社交互动到国家安全事务。然而，在现实场景中，可靠的欺骗行为指标仍然难以捉摸。
通过整合电生理和交际方法，我们展示了一种新的、客观的检测方法，用于识别双人欺骗任务互动场景中参与者特定的欺骗行为指标。我们使用新型干式丝网印刷电极阵列记录参与者的面部肌肉活动，并应用机器学习算法根据短暂的面部反应来识别谎言。
平均准确率为 73%，我们将参与者分为两组：通过激活脸颊肌肉来揭示谎言的参与者和激活眉毛的参与者。我们发现参与者随着时间的推移说谎的次数更多，一些人会切换他们明显的肌肉群。此外，虽然这里报告的自动化分类器的性能优于未经训练的人类探测器，但它们的性能是相关的，这表明它们依赖于共享的特征。
我们的研究结果表明，在社交环境中使用可穿戴电极阵列检测人类谎言是可行的，并为未来关于欺骗表达个体差异的研究奠定了基础。
