Deception, Detection of,<scp>P</scp>300 Event‐Related Potential (<scp>ERP</scp>)

Rosenfeld, J. Peter; Greely, Henry T.

doi:10.1002/9780470061589.fsa1053

Cited by 2 publications

(1 citation statement)

References 35 publications

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“…From the beginning, starting with the pioneer work of Benussi [ 5 ], the identification of deceptive responses has mainly been based on the use of physiological measures [ 6 ]. More recently, reaction time (RT)-based techniques have been introduced.…”

Section: Introductionmentioning

confidence: 99%

The detection of faked identity using unexpected questions and mouse dynamics

2017

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The detection of faked identities is a major problem in security. Current memory-detection techniques cannot be used as they require prior knowledge of the respondent’s true identity. Here, we report a novel technique for detecting faked identities based on the use of unexpected questions that may be used to check the respondent identity without any prior autobiographical information. While truth-tellers respond automatically to unexpected questions, liars have to “build” and verify their responses. This lack of automaticity is reflected in the mouse movements used to record the responses as well as in the number of errors. Responses to unexpected questions are compared to responses to expected and control questions (i.e., questions to which a liar also must respond truthfully). Parameters that encode mouse movement were analyzed using machine learning classifiers and the results indicate that the mouse trajectories and errors on unexpected questions efficiently distinguish liars from truth-tellers. Furthermore, we showed that liars may be identified also when they are responding truthfully. Unexpected questions combined with the analysis of mouse movement may efficiently spot participants with faked identities without the need for any prior information on the examinee.

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Section: Introductionmentioning

confidence: 99%