The Detection of Malingering: A New Tool to Identify Made-Up Depression

Abstract

Major depression is a high-prevalence mental disease with major socio-economic impact, for both the direct and the indirect costs. Major depression symptoms can be faked or exaggerated in order to obtain economic compensation from insurance companies. Critically, depression is potentially easily malingered, as the symptoms that characterize this psychiatric disorder are not difficult to emulate. Although some tools to assess malingering of psychiatric conditions are already available, they are principally based on self-reporting and are thus easily faked. In this paper, we propose a new method to automatically detect the simulation of depression, which is based on the analysis of mouse movements while the patient is engaged in a double-choice computerized task, responding to simple and complex questions about depressive symptoms. This tool clearly has a key advantage over the other tools: the kinematic movement is not consciously controllable by the subjects, and thus it is almost impossible to deceive. Two groups of subjects were recruited for the study. The first one, which was used to train different machine-learning algorithms, comprises 60 subjects (20 depressed patients and 40 healthy volunteers); the second one, which was used to test the machine-learning models, comprises 27 subjects (9 depressed patients and 18 healthy volunteers). In both groups, the healthy volunteers were randomly assigned to the liars and truth-tellers group. Machine-learning models were trained on mouse dynamics features, which were collected during the subject response, and on the number of symptoms reported by participants. Statistical results demonstrated that individuals that malingered depression reported a higher number of depressive and non-depressive symptoms than depressed participants, whereas individuals suffering from depression took more time to perform the mouse-based tasks compared to both truth-tellers and liars. Machine-learning models reached a classification accuracy up to 96% in distinguishing liars from depressed patients and truth-tellers. Despite this, the data are not conclusive, as the accuracy of the algorithm has not been compared with the accuracy of the clinicians; this study presents a possible useful method that is worth further investigation.

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.…”

An Item-Level Analysis for Detecting Faking on Personality Tests: Appropriateness of Ideal Point Item Response Theory Models

“…Second, focus on the precision of item estimates, it is inherently more difficult to recover true item parameters for ideal point models with the normal probability density function model, if comparing with that for dominance models which derive item estimates with the normal ogive model (Brown and Maydeu-Olivares, 2010). Considering GGUM's mathematical complexity for estimation difficulties, some studies related to detect faking used other methods, for example, techniques based on reaction times, and scored invalidity scales (Sellbom and Bagby, 2010;Monaro et al, 2018;Roma et al, 2018;Mazza et al, 2019), generally obtained superior accurate outcomes. Finally, practically speaking, the use of ideal point models seems not to result in any improvement for predictive validity, if comparing with dominance models (Zhang et al, 2019).…”

“…Many methodologies and techniques have been developed for detecting response distortion over the years, for example, machine learning models, reaction times, regression analysis, etc. (Dunn et al, 1972;Sellbom and Bagby, 2010;Jiménez Gómez et al, 2013;Monaro et al, 2018;Roma et al, 2018;Mazza et al, 2019). Still, there is a concern about the perceptions and interpretations of the change on items due to intentional dissimulation.…”

Spotting Faked Identities via Mouse Dynamics Using Complex Questions

“…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.…”