False Identity Detection Using Complex Sentences

Monaro, Merylin; Gamberini, Luciano; Zecchinato, Francesca; Sartori, Giuseppe

doi:10.3389/fpsyg.2018.00283

Cited by 15 publications

(20 citation statements)

References 37 publications

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“…In the literature, mouse dynamics have been shown to provide useful behavioural cues to identify deception 33,64 , and the technique has already been successfully applied to detect faking-bad respondents 5,47 . In the present research, only for the L scale the results were consistent with the findings reported in previous studies, which have shown that, compared to honest participants, fakers take more time to respond to stimuli 18 and outline wider trajectories when selecting a response 5 , albeit with small observed effects. Only in relation to the L scale, indeed, the results supported the fourth hypothesis (H4); on this scale, mouse movements were slower in the faking-good condition relative to the honest condition, since faking-good respondents spent more time than honest respondents in responding.…”

Section: Differences In Mouse Movements and Trajectories Between Honesupporting

confidence: 93%

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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Section: Differences In Mouse Movements and Trajectories Between Honesupporting

confidence: 93%

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

Mazza

Monaro

Burla

et al. 2020

Sci Rep

Self Cite

View full text Add to dashboard Cite

show abstract

“…In a previous study, Monaro et al (Monaro et al, 2018b) have named "complex questions" the sentences that contain more than one information in the same phrase. For example, to investigate the identity one could ask a question about the name (e.g., Is Alice your name?)…”

Section: The Cognitive Theory Under Complex Sentencesmentioning

confidence: 99%

“…While truth-tellers can speedily carry out this sequence of mental operations, liars need more time to match the plausibility of each information with the lie they told (Williams et al, 2013). As result, liars have a bad performance, compared with truth-tellers, when they are involved in a decision task, making a greater number of errors and showing slower reaction times (Monaro et al, 2018b).…”

Section: The Cognitive Theory Under Complex Sentencesmentioning

confidence: 99%

“…An example of stimuli is reported in Table1. All the stimuli, including complex sentences, were built according to those reported by Monaro et al (Monaro et al, 2018b).…”

Section: Stimulimentioning

confidence: 99%

“…The aim of this paper is to validate the mouse dynamics as a tool to detect deception using an alternative technique to unexpected questions to induce cognitive load in liars. Particularly, here we propose the use of complex sentences (Monaro et al, 2018b;Monaro et al, 2018c), which exceed the mentioned above limit of the unexpected questions.…”

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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Multiple psychological characteristics predict housing mortgage loan behavior: A holistic model based on machine learning

Gui

Wang

et al. 2022

PsyCh Journal

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The factors that influence consumers' house choice are debatable. Previous studies have examined the effects of demographic and socioeconomic attributes, physical and environmental features of the house, and isolated single psychological characteristics on housing behavior. However, these factors are still not sufficient to predict consumer housing behavior, particularly when they are measured separately. We construct a holistic model that integrates psychological characteristics including values, personality traits, motivation, decision‐making style, and risk‐seeking together with demographic and socioeconomic factors to jointly predict housing mortgage loan behavior. This study aims to use a newly developed statistical method, “machine learning,” to examine the relationship between multiple psychological characteristics and consumer housing mortgage loan behavior. Data were collected through an online survey (N = 2,270). The results show that the holistic psychological model is effective for predicting consumer housing mortgage loan behavior in the life context. Moreover, by analyzing and comparing the relative impact of all predictors, we find that psychological characteristics made a more important contribution to predicting housing mortgage loan behavior than did traditional factors (demographic and socioeconomic factors). The results provide a new perspective for understanding the effects of how multiple psychological characteristics integrally predict consumers' housing mortgage loan behavior in the real estate market. Theoretical and practical implications for marketing and sales are discussed.

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False Identity Detection Using Complex Sentences

Cited by 15 publications

References 37 publications

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

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

Spotting Faked Identities via Mouse Dynamics Using Complex Questions

Multiple psychological characteristics predict housing mortgage loan behavior: A holistic model based on machine learning

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