Original Research ARTICLE
Introducing machine learning to detect personality faking-good: A new model based on MMPI-2-RF scales and reaction times
- 1Department of Human Neurosciences, Faculty of Medicine and Dentistry, Sapienza University of Rome, Italy
- 2Department of General Psychology, University of Padova, Italy
- 3Department of Surgical, Medical, Molecular and Critical Pathology, University of Pisa, Italy
BACKGROUND AND PURPOSE. The use of machine learning (ML) models in the detection of malingering has yielded encouraging results, showing promising accuracy levels. We investigated the possible application of this methodology when trained on behavioral features, such as response time (RT) and time pressure, to identify faking behavior in self-report personality questionnaires. To do so, we reintroduced the article of Roma et al. (2018), which highlighted that RTs and time pressure are useful variables in the detection of faking; we then extended the number of participants and applied an ML analysis.
MATERIALS AND METHODS. The sample was comprised of 175 subjects, of whom all were graduates (having completed at least 17 years of instruction), male, and Caucasian. Subjects were randomly assigned to four groups: Honest Speeded, Faking-Good Speeded, Honest Un-Speeded, and Faking-Good Un-Speeded. A software version of the MMPI-2-RF was administered.
RESULTS. Results indicated that ML algorithms reached very high accuracies (around 95%) in detecting malingerers when subjects are instructed to respond under time pressure. The classifiers performance was lower when the subjects responded with no time restriction to the MMPI-2-RF items, with accuracies ranging from 75% to 85%. Further analysis demonstrated that T scores of validity scales are ineffective to detect fakers when participants were not under temporal pressure (accuracies 55%-65%), whereas temporal features resulted to be more useful (accuracies 70%-75%). By contrast, temporal features and T scores of validity scales are equally effective in detecting fakers when subjects are under time pressure (accuracies higher than 90%).
DISCUSSION. To conclude, results demonstrated that ML techniques are extremely valuable and reach high performance in detecting fakers in self-report personality questionnaires, over more the traditional psychometric techniques. Validity scales MMPI-2-RF manual criteria are very poor in identifying under-reported profiles. Moreover, temporal measures are useful tools in distinguishing honest from dishonest responders, especially in a no time pressure condition. Indeed, time pressure brings out malingerers in clearer way compared to no time pressure condition.
Keywords: MMPI-2-RF, Faking-good, Machine leaming, response latency, Time pressure
Received: 13 Mar 2019;
Accepted: 16 May 2019.
Edited by:Cristina Scarpazza, University of Padova, Italy
Reviewed by:Marije E. Keulen-de Vos, Forensic Psychiatric Center (FPC), Netherlands
Ruth J. Tully, Tully Forensic Psychology Ltd, United Kingdom
Copyright: © 2019 Mazza, Monaro, Orrù, Burla, Colasanti, Ferracuti and Roma. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Prof. Paolo Roma, Department of Human Neurosciences, Faculty of Medicine and Dentistry, Sapienza University of Rome, Rome, Italy, email@example.com