ISSN 1671-3710
CN 11-4766/R
主办:中国科学院心理研究所
出版:科学出版社

心理科学进展 ›› 2020, Vol. 28 ›› Issue (10): 1619-1630.doi: 10.3724/SP.J.1042.2020.01619

• 研究构想 • 上一篇    下一篇

认知负荷取向下基于记忆-反应冲突的欺骗检测

梁静1, 阮倩男2, 李贺3, 马梦晴1, 颜文靖2()   

  1. 1鲁东大学教育科学学院, 烟台 264025
    2温州大学心理与行为科学研究所, 温州 325015
    3西北大学公共管理学院, 西安 710127
  • 收稿日期:2019-11-15 出版日期:2020-10-15 发布日期:2020-08-24
  • 通讯作者: 颜文靖 E-mail:eagan-ywj@foxmail.com
  • 基金资助:
    * 国家自然科学基金(31900761);国家自然科学基金(31500875);山东省自然科学基金(ZR2018PC034);山东省高等学校青创科技支持计划(2019RWF001);浙江省自然科学基金(LQ16C090002);陕西省教育厅科研计划项目(18JK0726)

Deception detection based on memory-response conflict: A cognitive load approach

LIANG Jing1, RUAN Qiannan2, LI He3, MA Mengqing1, YAN Wenjing2()   

  1. 1School of Educational Science, Ludong University, Yantai 264025, China
    2Institute of Psychology and Behavior Sciences, Wenzhou University, Wenzhou 264025, China
    3School of Public Administration, Northwest University, Xi’an 710127, China
  • Received:2019-11-15 Online:2020-10-15 Published:2020-08-24
  • Contact: YAN Wenjing E-mail:eagan-ywj@foxmail.com

摘要:

欺骗检测一直是心理学的重要研究问题。基于欺骗理论的认知视角, 研究者提出欺骗检测的认知负荷取向。采用隐瞒信息测试这一测谎范式, 通过操纵认知负荷影响个体在虚假反应时的记忆-反应冲突解决过程, 考察增加认知负荷对欺骗检测的影响, 以期更好地揭示欺骗检测的认知机制。在此基础上, 以普通人群和犯罪嫌疑人为被试探查基于记忆-反应冲突的欺骗检测的行为和生理指标, 并根据获得的行为和生理指标, 采用机器学习方法进行建模, 预测个体的欺骗行为。研究结果将服务于司法、安防和人际交往等领域的欺骗检测。

关键词: 欺骗检测, 认知负荷, 记忆-反应冲突, 干扰任务, 机器学习

Abstract:

Deception detection is an important topic in psychology. The cognitive approach to deception detection is based on the premise that lying is more cognitively demanding than truth telling. Increased cognitive load is hypothesized to result in greater behavioral differences between truth tellers and liars. By manipulating cognitive load through different interfering task of various difficulties during the concealed information test, the influence of cognitive load on memory-response conflict was investigated to better illustrate the cognitive mechanism of deception detection. Second, behavioral and physiological cues for memory-response conflict based deception detection were examined in both noncriminal and criminal group. Finally, machine learning algorithms were employed to predict liars and truth tellers via behavioral and physiological cues. These findings will serve to aid in deception detection in the fields of judicial security and human communication.

Key words: deception detection, cognitive load, memory-response conflict, interfering task, machine learning

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