An Eye Tracking Study on Feigned Schizophrenia

Research on malingering detection has not yet taken full advantage of eye tracking technology. In particular, while several studies indicate that patients with schizophrenia behave notably differently from controls on specific oculomotor tasks, no study has yet investigated whether experimental participants instructed to feign could reproduce those behaviors, if coached to do so. Due to the automatic nature of eye movements, we anticipated that eye tracking analyses would help detect feigned schizophrenic problems. To test this hypothesis, we recorded the eye movements of 83 adult UK volunteers, and tested whether eye movements of healthy volunteers instructed to feign schizophrenia (n = 43) would differ from those of honest controls (n = 40), while engaging in smooth pursuit and pro- and anti-saccade tasks. Additionally, results from our investigation were also compared against previously published data observed in patients with schizophrenia performing similar oculomotor tasks. Data analysis showed that eye movements of experimental participants instructed to feign (a) only partially differed from those of controls and (b) did not closely resemble those from patients with schizophrenia reported in previously published papers. Taken together, these results suggest that examination of eye movements does have the potential to help detecting feigned schizophrenia.

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%.

Feigning memory impairment in a forced-choice task: Evidence from event-related potentials

Strategies of malingering detection have brought about a wealth of neuropsychological studies in the last decades. However, the investigation of physiological measures to reliably differentiate between authentic and manipulated symptom presentations is still in its infancy. The present study examined event-related potentials (ERP) to identify feigned memory impairment. We tested instructed malingerers (n = 25) and control participants (n = 22) with a recognition task similar to the Test of Memory Malingering. No differences between groups were found for P1 (70-110 ms) but for N1 (120-170 ms) and P300 components, with lower amplitudes for instructed malingerers. Behavioral data showed a typical pattern of unrealistically high errors in a forced-choice recognition task and less overall recalled stimuli in instructed malingerers. We also found study-phase repetition and old/new effects in the P300, but no interactions with groups (control vs. malingering). Post-hoc analyses revealed that the P300 effect is greater when participants reported an attention-based faking strategy, as opposed to response-based malingerers and controls. The employment of physiological measures can yield additional information on the validity of test data without the need to perform additional tests.