How Human-Mouse Interaction can Accurately Detect Faked Responses About Identity

Monaro, Merylin; Fugazza, Francesca Ileana; Gamberini, Luciano; Sartori, Giuseppe

doi:10.1007/978-3-319-57753-1_10

Cited by 12 publications

(11 citation statements)

References 23 publications

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“…On the contrary, liars show wider trajectories, characterized by a greater AUC and MD. This visual pattern is in line with those found by Monaro et al (Monaro et al, 2017c;Monaro et al, 2017a;Monaro et al, 2017b) observing motor trajectories on unexpected questions. Focusing on complex stimuli, we split the trajectories of questions requiring a "yes" response from those requiring a "no" response (see Figure 3).…”

Section: Analysis Of Trajectoriessupporting

confidence: 91%

“…The first step in this direction was recently taken by Monaro et al (Monaro et al, 2017c;Monaro et al, 2017a;Monaro et al, 2017b) who proposed a technique based on unexpected questions and the recording of mouse dynamics. Compared to the other existing cognitive-based lie detection methods (e.g., the autobiographical Implicit Association Test (aIAT; Agosta & Sartori, 2013) or the Concealed Information Test (CIT; Ben-Shakhar, 2012)) this was the first attempt to create a tool that works without any ground truth.…”

Section: Introductionmentioning

confidence: 99%

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Spotting Faked Identities via Mouse Dynamics Using Complex Questions

Monaro

Gamberini

Giuseppe

2018

Electronic Workshops in Computing

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The increment of criminals, including terrorists, crossing international borders using faked identities is a crucial issue. This paper validates a computerized technique to spot people who declare false identity information. Forty participants were asked to answer complex questions about their identity, clicking with the mouse on the correct alternative response on the computer screen. Half of the participants answered truthfully, while the others were instructed to lie. As long as the subject responded to questions, mouse dynamics were recorded. Because lying is cognitively demanding, liars had fewer cognitive resources available to analyse complex questions and to compute the response. As result, they showed a bad performance in the task compared with truth-tellers, revealing a greater number of errors, slower reaction times and larger mouse trajectories. Different machine learning classifiers were trained by a cross validation procedure, achieving a classification accuracy up to 90% in detecting liars.

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Section: Analysis Of Trajectoriessupporting

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Spotting Faked Identities via Mouse Dynamics Using Complex Questions

Monaro

Gamberini

Giuseppe

2018

Electronic Workshops in Computing

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“…These problems are not currently solved, even if the research is putting a great effort in this direction, also studying new lie detection techniques with possible application in real and online environment (Verschuere and Kleinberg, 2016 ; Monaro et al, 2017a , c ).…”

Section: Introductionmentioning

confidence: 99%

False Identity Detection Using Complex Sentences

et al. 2018

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The use of faked identities is a current issue for both physical and online security. In this paper, we test the differences between subjects who report their true identity and the ones who give fake identity responding to control, simple, and complex questions. Asking complex questions is a new procedure for increasing liars' cognitive load, which is presented in this paper for the first time. The experiment consisted in an identity verification task, during which response time and errors were collected. Twenty participants were instructed to lie about their identity, whereas the other 20 were asked to respond truthfully. Different machine learning (ML) models were trained, reaching an accuracy level around 90–95% in distinguishing liars from truth tellers based on error rate and response time. Then, to evaluate the generalization and replicability of these models, a new sample of 10 participants were tested and classified, obtaining an accuracy between 80 and 90%. In short, results indicate that liars may be efficiently distinguished from truth tellers on the basis of their response times and errors to complex questions, with an adequate generalization accuracy of the classification models.

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“…Although RTs recorded by mouse tracking do not necessarily overlap with the RTs registered in the aforementioned studies, the mouse tracking technique has nonetheless proven useful for lie detection 33 . Previous studies [33][34][35] have shown that, when half of a sample answered an autobiographical questionnaire truthfully and the other half answered according to fake profiles that had been learned just prior to testing, honest participants followed the more direct trajectory to the desired answer while fakers showed trajectories that initially converged towards the actual autobiographical information and then switched to the opposite direction to select the relevant alternative.The present study aimed at generating insight into the relationship between different approaches to identifying faking-good behaviour on the underreporting validity scales of two widely used personality questionnaires: the L, K, and S underreporting scales of the MMPI-2 and the Virtuous Responding (VR) scale of the PPI-R. These scales were chosen because they were designed to detect the acknowledgment of uncommon virtues and the tendency to omit negative features of personality in order to present oneself in a better light.…”

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confidence: 98%

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confidence: 98%

Use of mouse-tracking software to detect faking-good behavior on personality questionnaires: an explorative study

Mazza

Monaro

Burla

et al. 2020

Sci Rep

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the aim of the present study was to explore whether kinematic indicators could improve the detection of subjects demonstrating faking-good behaviour when responding to personality questionnaires. one hundred and twenty volunteers were randomly assigned to one of four experimental groups (honest unspeeded, faking-good unspeeded, honest speeded, and faking-good speeded). Participants were asked to respond to the MMPI-2 underreporting scales (L, K, S) and the PPI-R Virtuous Responding (VR) scale using a computer mouse. The collected data included T-point scores on the L, K, S, and VR scales; response times on these scales; and several temporal and spatial mouse parameters. these data were used to investigate the presence of significant differences between the two manipulated variables (honest vs. faking-good; speeded vs. unspeeded). The results demonstrated that T-scores were significantly higher in the faking-good condition relative to the honest condition; however, fakinggood and honest respondents showed no statistically significant differences between the speeded and unspeeded conditions. Concerning temporal and spatial kinematic parameters, we observed mixed results for different scales and further investigations are required. The most consistent finding, albeit with small observed effects, regards the L scale, in which faking-good respondents took longer to respond to stimuli and outlined wider mouse trajectories to arrive at the given response.One of the main limitations of the use of self-report questionnaires to assess personality is that such tests are vulnerable to faking behavior 1 -that is, the tendency to deliberately distort one's responses in order to fulfil personal goals 2 . In one form of faking, respondents exaggerate or create symptoms to emphasize their psychological suffering and discomfort (faking-bad); in another, respondents present themselves in a particularly favourable fashion, emphasizing their desirable traits and rejecting their undesirable ones (faking-good). Faking behaviour is widespread in many contexts, with alarming estimates of prevalence (e.g., 30-50% in personnel selection 3 and up to 30% in forensic settings 4,5 ). Many studies have focused on faking-bad behaviour and developed tools to facilitate its detection; such tools include the Structured Interview of Reported Symptoms-2 (SIRS-2) 6 , the Structured Inventory of Malingered Symptomatology (SIMS) 7 , and the Inventory of Problems-29 8 . Faking-bad behaviour has received more research attention 9,10 perhaps because its welfare/social costs (in terms of, e.g. insurance compensation) are more easily recognizable; thus, the literature on the subject is not as rich and instruments to identify faking-good behaviour are lacking; for this reason, the present study focused on faking-good behavior, specifically.Analysis of validity scales is one of the most commonly used methods to detect fakers. Validity scales were designed to gather information on the validity and interpretability of self-report questionnaires by exploring the ...

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How Human-Mouse Interaction can Accurately Detect Faked Responses About Identity

Cited by 12 publications

References 23 publications

Spotting Faked Identities via Mouse Dynamics Using Complex Questions

Spotting Faked Identities via Mouse Dynamics Using Complex Questions

False Identity Detection Using Complex Sentences

Use of mouse-tracking software to detect faking-good behavior on personality questionnaires: an explorative study

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