利用比较问题测试 (CQT) 评估检测欺骗行为的生理指标

A M Shahruj Rashid, Bryan Carmichael, Charlize Su, Keming Shi, Keefe Lim, Poorvika Senthil Kumar, Ngok Jeun Wan, Eshaan Govil, Dennis Yap
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摘要

尽管在欺骗检测方面取得了重大进展,但传统方法在实际应用中往往存在不足。本研究通过评估瞳孔反应、皮电活动 (EDA)、心率 (HR) 和面部温度变化等各种生理指标在使用比较问题测试 (CQT) 预测欺骗行为方面的有效性,解决了这些局限性。该研究还填补了一项重要的研究空白,在亚洲背景下验证了这些方法。研究采用了主体间设计,从 118 名新加坡参与者(包括华人、印度人和马来人)的不同样本中收集数据。研究旨在确定哪些生理指标结合在一起能对欺骗行为做出最可靠的预测。研究的主要创新点包括将 CQT 改编为经过修改的定向谎言范式,并扩大样本量以评估每种生理指标的相对重要性。研究结果表明,瞳孔反应是预测欺骗行为的最重要指标,EDA 增强了模型的解释力。心率虽然相关,但与瞳孔反应和 EDA 结合使用时增加的价值有限,面部温度变化在统计上也不显著。这项研究强调了进一步研究生理指标之间的相互作用及其在不同情况下应用的必要性。这项研究为改进欺骗检测方法提供了有价值的见解,并为未来的研究奠定了基础,未来的研究可能会纳入更多的生理指标并探索现实世界中的应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Evaluating Physiological Indicators in Detecting Deception using the Comparison Question Test (CQT)
Despite significant advancements in deception detection, traditional methods often fall short in real-world applications. This study addresses these limitations by evaluating the effectiveness of various physiological measures Pupil Response, Electrodermal Activity (EDA), Heart Rate (HR), and facial temperature changes in predicting deception using the Comparison Question Test (CQT). It also fills a critical research gap by validating these methods within an Asian context. Employing a between-subjects design, data was collected from a diverse sample of 118 participants from Singapore, including Chinese, Indian, and Malay individuals. The research aims to identify which physiological indicators, in combination, offer the most robust predictions of deceptive behavior. Key innovations include the adaptation of the CQT with a modified directed lie paradigm and an expanded sample size to assess the relative importance of each physiological measure. The study's findings reveal that Pupil Response is the most significant predictor of deception, with EDA enhancing the model's explanatory power. HR, while relevant, adds limited value when combined with Pupil Response and EDA, and facial temperature changes were statistically non-significant. The study highlights the need for further research into the interactions among physiological measures and their application in varied contexts. This research contributes valuable insights into improving deception detection methodologies and sets the stage for future investigations that could incorporate additional physiological indicators and explore real-world applications.
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