The detection of faked identity using unexpected questions and choice reaction times
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ORIGINAL ARTICLE
The detection of faked identity using unexpected questions and choice reaction times Merylin Monaro1 · Ilaria Zampieri2 · Giuseppe Sartori1 · Pietro Pietrini2 · Graziella Orrù3 Received: 2 April 2020 / Accepted: 25 August 2020 © The Author(s) 2020
Abstract The identification of faked identities, especially within the Internet environment, still remains a challenging issue both for companies and researchers. Recently, however, latency-based lie detection techniques have been developed to evaluate whether the respondent is the real owner of a certain identity. Among the paradigms applied to this purpose, the technique of asking unexpected questions has proved to be useful to differentiate liars from truth-tellers. The aim of the present study was to assess whether a choice reaction times (RT) paradigm, combined with the unexpected question technique, could efficiently detect identity liars. Results demonstrate that the most informative feature in distinguishing liars from truth-tellers is the Inverse Efficiency Score (IES, an index that combines speed and accuracy) to unexpected questions. Moreover, to focus on the predictive power of the technique, machine-learning models were trained and tested, obtaining an out-of-sample classification accuracy of 90%. Overall, these findings indicate that it is possible to detect liars declaring faked identities by asking unexpected questions and measuring RTs and errors, with an accuracy comparable to that of well-established latency-based techniques, such as mouse and keystroke dynamics recording.
Introduction Millions of people have their identities stolen every year. There is no fool-proof way to pinpoint fakers, especially when faked identities are used to register online. Traditional methods of lie detection include face-to-face interviews and polygraphs that measure heart rate and skin conductance (Granhag, Vrij, & Verschuere, 2015). Leaving aside the debated accuracy of the polygraph, these techniques cannot be used remotely or with large numbers of people.
Merylin Monaro and Ilaria Zampieri contributed equally to the paper. Electronic supplementary material The online version of this article (https://doi.org/10.1007/s00426-020-01410-4) contains supplementary material, which is available to authorized users. * Merylin Monaro [email protected] 1
Department of General Psychology, University of Padua, Padua, Italy
2
IMT School for Advanced Studies, Lucca, Italy
3
Department of Surgical, Medical and Molecular Pathology and Critical Care Medicine, University of Pisa, Pisa, Italy
Recently, researchers have developed latency-based measures to determine whether the respondent is the real owner of a certain identity (Sartori, Zangrossi, & Monaro, 2018). Latency-based lie detection techniques find their roots in the cognitive load theory, according to which lying requires a greater cognitive effort than truth-telling; this higher workload is reflected by a number of indices, including, for example, reaction times (RT) (Vrij, Fisher, &
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