The detection of faked identity using unexpected questions and mouse dynamics

Abstract

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.

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

“…During the experimental task, the MouseTracker software 25 automatically recorded a number of features relating to the response of the mouse in spatial and temporal terms. Mouse parameters that the literature reported to be the most sensitive to deception detection were collected 5,33,35,47 . Specifically, the following features were captured for each mouse trajectory: The idealized trajectory represented the virtual straight line connecting the starting point to the endpoint (the response label).…”

“…In the present study, mouse dynamics were used for the first time to investigate faking-good behaviour with respect to the validity scales of two personality questionnaires (the MMPI-2 and PPI-R). 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.…”

“…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.…”

“…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.…”

A State Space Approach to Dynamic Modeling of Mouse-Tracking Data

“…For instance, the curvature of computer-mouse trajectories might reveal competing processes activated in discriminating the two categories. Mouse-tracking has been successfully applied in several cognitive research studies, including lexical decision [41,37], social categorization [24,11], numerical cognition [19,20], memory [54], moral decision [43], and lie detection [50]. Moreover, the availability of specialized and freely-available software for mouse-tracking experiments have strongly contributed to the wide-spread application of such a methodology in the more general psychological domain [22,42].…”