A novel methodology for the objective ascertainment of psychic and existential damage

Ferrara, Santo Davide; Ananian, Viviana; Baccino, É.; Boscolo‐Berto, Rafael; Domenici, Ranieri; Hernández-Cueto, C.; Mendelson, George; Norelli, Gian Aristide; Ranavaya, Mohammed; Terranova, Claudio; Vieira, Duarte Nuno; Viel, Guido; Villanueva, Enrique; Zoia, R.; Sartori, Giuseppe

doi:10.1007/s00414-016-1366-8

Cited by 8 publications

(3 citation statements)

References 22 publications

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“…For example, the autobiographical Implicit Association Test [aIAT; ( 20 )] and the Concealed Information Test [CIT; ( 21 )] are able to identify liars based on their response time (TR). Concerning malingering detection, the aIAT has been successfully applied to detect whiplash malingering, confirming an accuracy of around 90% ( 22 ), as well as to unveil phantom-limb pain ( 13 ) and psychogenic amnesia ( 16 ). The CIT has been principally used to assess the simulation of amnesia ( 21 ).…”

Section: Introductionmentioning

confidence: 89%

“…Currently, a diagnosis of a psychiatric disorder, including depression, is formulated according to the subjective experience that is reported by the patient and to the observation of signs and non-verbal behavior that are easily manipulated by the individual will to deceive ( 12 ). In other words, the psychic symptom exists because the patient refers to it, and the assessment of malingering is mostly based on clinical judgment ( 13 ). While this aspect is less (or none) than a problem in the clinical setting, in which patients are seeking help for their sufferance and in which malingering itself become a symptom (as in the case of factious disorder or Munchausen syndrome), the forensic context is a quite different situation.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2018

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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.

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Section: Introductionmentioning

confidence: 89%

Section: Introductionmentioning

confidence: 99%

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

et al. 2018

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“…The aIAT has an impressive 91% accuracy rate in identifying those possessing genuine autobiographical memories (108) and has proven effective in uncovering the malingering of whiplash (109), and unveiling phantom limb pain (110). Clearly those genuinely affected by cognitive or psychiatric impairments should have many life memories of experienced symptomology that can be probed.…”

Section: Lie Detectionmentioning

confidence: 99%

A Review of Approaches to Detecting Malingering in Forensic Contexts and Promising Cognitive Load-Inducing Lie Detection Techniques

2018

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Malingering, the feigning of psychological or physical ailment for gain, imposes high costs on society, especially on the criminal-justice system. In this article, we review some of the costs of malingering in forensic contexts. Then the most common methods of malingering detection are reviewed, including those for feigned psychiatric and cognitive impairments. The shortcomings of each are considered. The article continues with a discussion of commonly used means for detecting deception. Although not traditionally used to uncover malingering, new, innovative methods are emphasized that attempt to induce greater cognitive load on liars than truth tellers, some informed by theoretical accounts of deception. As a type of deception, we argue that such cognitive approaches and theoretical understanding can be adapted to the detection of malingering to supplement existing methods.

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