The Detection of Malingered Amnesia: An Approach Involving Multiple Strategies in a Mock Crime

Detecting lies in investigative interviews through the analysis of response latencies and error rates to unexpected questions

In this study, we propose an approach to detect deception during investigative interviews by integrating response latency and error analysis with the unexpected question technique. Sixty participants were assigned to an honest (n = 30) or deceptive group (n = 30). The deceptive group was instructed to memorize the false biographical details of a fictitious identity. Throughout the interviews, participants were presented with a randomized sequence of control, expected, and unexpected open-ended questions about identity. Responses were audio recorded for detailed examination. Our findings indicate that deceptive participants showed markedly longer latencies and higher error rates when answering expected (requiring deception) and unexpected questions (for which premeditated deception was not possible). Longer response latencies were also observed in participants attempting deception when answering control questions (which necessitated truthful answers). Moreover, a within-subject analysis highlighted that responding to unexpected questions significantly impaired individuals' performance compared to answering control and expected questions. Leveraging machine-learning algorithms, our approach attained a classification accuracy of 98% in distinguishing deceptive and honest participants. Additionally, a classification analysis on single response levels was conducted. Our findings underscore the effectiveness of merging response latency metrics and error rates with unexpected questioning as a robust method for identity deception detection in investigative interviews. We also discuss significant implications for enhancing interview strategies.

Two Cases of Malingered Crime-Related Amnesia

Amnesia is a frequent claim in major crimes, and it is estimated that the complete or partial absence of memory following a crime ranges from 25% to 50% of total cases. Although some cases may constitute a genuine form of amnesia, due to organic-neurological defects or psychological causes, and possibly combined with a dissociative or repressive coping style after an extreme experience, malingering is still fairly common in offenders. Therefore, one of the main goals in medico-legal proceedings is to find methods to determine the credibility of crime-related amnesia. At present, a number of lie and memory detection techniques can assist the forensic assessment of the reliability of declarative proof, and have been devised and improved over the past century: for example, modern polygraphs, event-related potentials, thermal imaging, functional magnetic resonance imaging, kinematic, and facial analysis. Other ad hoc psychological tests, such as the so-called Symptom Validity Test (SVT) and Performance Validity Test (PVT), as well as the autobiographical Implicit Association Test (aIAT), can also be used. To date, however, there is little evidence or case reports that document their real usefulness in forensic practice. Here, we report two cases of crime-related amnesia, whereby both defendants, who were found guilty of homicide, appeared to exhibit dissociative amnesia but where the application of SVTs, PVTs, and aIAT detected a malingered amnesia.

Symptom and Performance Validity Assessment in European Countries: an Update

In 2013, a special issue of the Spanish journal Clínica y Salud published a review on symptom and performance validity assessment in European countries (Merten et al. in Clínica y Salud, 24(3), 129–138, 2013). At that time, developments were judged to be in their infancy in many countries, with major publication activities stemming from only four countries: Spain, The Netherlands, Great Britain, and Germany. As an introduction to a special issue of Psychological Injury and Law, this is an updated report of developments during the last 10 years. In that period of time, research activities have reached a level where it is difficult to follow all developments; some validity measures were newly developed, others were adapted for European languages, and validity assessment has found a much stronger place in real-world evaluation contexts. Next to an update from the four nations mentioned above, reports are now given from Austria, Italy, and Switzerland, too.

The detection of malingering in whiplash-related injuries: a targeted literature review of the available strategies

OBJECTIVE

The present review is intended to provide an up-to-date overview of the strategies available to detect malingered symptoms following whiplash. Whiplash-associated disorders (WADs) represent the most common traffic injuries, having a major impact on economic and healthcare systems worldwide. Heterogeneous symptoms that may arise following whiplash injuries are difficult to objectify and are normally determined based on self-reported complaints. These elements, together with the litigation context, make fraudulent claims particularly likely. Crucially, at present, there is no clear evidence of the instruments available to detect malingered WADs.

METHODS

We conducted a targeted literature review of the methodologies adopted to detect malingered WADs. Relevant studies were identified via Medline (PubMed) and Scopus databases published up to September 2020.

RESULTS

Twenty-two methodologies are included in the review, grouped into biomechanical techniques, clinical tools applied to forensic settings, and cognitive-based lie detection techniques. Strengths and weaknesses of each methodology are presented, and future directions are discussed.

CONCLUSIONS

Despite the variety of techniques that have been developed to identify malingering in forensic contexts, the present work highlights the current lack of rigorous methodologies for the assessment of WADs that take into account both the heterogeneous nature of the syndrome and the possibility of malingering. We conclude that it is pivotal to promote awareness about the presence of malingering in whiplash cases and highlight the need for novel, high-quality research in this field, with the potential to contribute to the development of standardised procedures for the evaluation of WADs and the detection of malingering.

A model to differentiate WAD patients and people with abnormal pain behaviour based on biomechanical and self-reported tests

The prevalence of malingering among individuals presenting whiplash-related symptoms is significant and leads to a huge economic loss due to fraudulent injury claims. Various strategies have been proposed to detect malingering and symptoms exaggeration. However, most of them have been not consistently validated and tested to determine their accuracy in detecting feigned whiplash. This study merges two different approaches to detect whiplash malingering (the mechanical approach and the qualitative analysis of the symptomatology) to obtain a malingering detection model based on a wider range of indices, both biomechanical and self-reported. A sample of 46 malingerers and 59 genuine clinical patients was tested using a kinematic test and a self-report questionnaire asking about the presence of rare and impossible symptoms. The collected measures were used to train and validate a linear discriminant analysis (LDA) classification model. Results showed that malingerers were discriminated from genuine clinical patients based on a greater proportion of rare symptoms vs. possible self-reported symptoms and slower but more repeatable neck motions in the biomechanical test. The fivefold cross-validation of the LDA model yielded an area under the curve (AUC) of 0.84, with a sensitivity of 77.8% and a specificity of 84.7%.

Translational Application of a Neuro-Scientific Multi-Modal Approach Into Forensic Psychiatric Evaluation: Why and How?

A prominent body of literature indicates that insanity evaluations, which are intended to provide influential expert reports for judges to reach a decision "beyond any reasonable doubt," suffer from a low inter-rater reliability. This paper reviews the limitations of the classical approach to insanity evaluation and the criticisms to the introduction of neuro-scientific approach in court. Here, we explain why in our opinion these criticisms, that seriously hamper the translational implementation of neuroscience into the forensic setting, do not survive scientific scrutiny. Moreover, we discuss how the neuro-scientific multimodal approach may improve the inter-rater reliability in insanity evaluation. Critically, neuroscience does not aim to introduce a brain-based concept of insanity. Indeed, criteria for responsibility and insanity are and should remain clinical. Rather, following the falsificationist approach and the convergence of evidence principle, the neuro-scientific multimodal approach is being proposed as a way to improve reliability of insanity evaluation and to mitigate the influence of cognitive biases on the formulation of insanity opinions, with the final aim to reduce errors and controversies.

Detecting faking-good response style in personality questionnaires with four choice alternatives

Deliberate attempts to portray oneself in an unrealistic manner are commonly encountered in the administration of personality questionnaires. The main aim of the present study was to explore whether mouse tracking temporal indicators and machine learning models could improve the detection of subjects implementing a faking-good response style when answering personality inventories with four choice alternatives, with and without time pressure. A total of 120 volunteers were randomly assigned to one of four experimental groups and asked to respond to the Virtuous Responding (VR) validity scale of the PPI-R and the Positive Impression Management (PIM) validity scale of the PAI via a computer mouse. A mixed design was implemented, and predictive models were calculated. The results showed that, on the PIM scale, faking-good participants were significantly slower in responding than honest respondents. Relative to VR items, PIM items are shorter in length and feature no negations. Accordingly, the PIM scale was found to be more sensitive in distinguishing between honest and faking-good respondents, demonstrating high classification accuracy (80–83%).

The Development of a Short Version of the SIMS Using Machine Learning to Detect Feigning in Forensic Assessment

In the present study, we applied machine learning techniques to evaluate whether the Structured Inventory of Malingered Symptomatology (SIMS) can be reduced in length yet maintain accurate discrimination between consistent participants (i.e., presumed truth tellers) and symptom producers. We applied machine learning item selection techniques on data from Mazza et al. (2019c) to identify the minimum number of original SIMS items that could accurately distinguish between consistent participants, symptom accentuators, and symptom producers in real personal injury cases. Subjects were personal injury claimants who had undergone forensic assessment, which is known to incentivize malingering and symptom accentuation. Item selection yielded short versions of the scale with as few as 8 items (to differentiate between consistent participants and symptom producers) and as many as 10 items (to differentiate between consistent and inconsistent participants). The scales had higher classification accuracy than the original SIMS and did not show the bias that was originally reported between false positives and false negatives.

The detection of faked identity using unexpected questions and choice reaction times

The identification of faked identities, especially within the Internet environment, still remains a challenging issue both for companies and researchers. Recently, however, latency-based lie detection techniques have been developed to evaluate whether the respondent is the real owner of a certain identity. Among the paradigms applied to this purpose, the technique of asking unexpected questions has proved to be useful to differentiate liars from truth-tellers. The aim of the present study was to assess whether a choice reaction times (RT) paradigm, combined with the unexpected question technique, could efficiently detect identity liars. Results demonstrate that the most informative feature in distinguishing liars from truth-tellers is the Inverse Efficiency Score (IES, an index that combines speed and accuracy) to unexpected questions. Moreover, to focus on the predictive power of the technique, machine-learning models were trained and tested, obtaining an out-of-sample classification accuracy of 90%. Overall, these findings indicate that it is possible to detect liars declaring faked identities by asking unexpected questions and measuring RTs and errors, with an accuracy comparable to that of well-established latency-based techniques, such as mouse and keystroke dynamics recording.

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

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, faking-good 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.