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Original Research ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Psychol. | doi: 10.3389/fpsyg.2019.01650

MALINGERING DETECTION OF COGNITIVE IMPAIRMENT WITH THE B TEST IS BOOSTED USING MACHINE LEARNING

 Giuseppe Sartori1*, Giorgia Pace1,  Graziella Orrù2,  Merylin Monaro1,  Francesca Gnoato1, Roberta Vitaliani3, Kyle B. Boone4 and Angelo Gemignani2
  • 1University of Padova, Italy
  • 2University of Pisa, Italy
  • 3Ca' Foncello Hospital, Italy
  • 4Alliant International University, United States

Objective: Here we report an investigation on the accuracy of the b Test, a measure to identify malingering of cognitive symptoms, in detecting malingerers of mild cognitive impairment.
Method: Three groups of participants, patients with Mild Neurocognitive Disorder (n=21), healthy elders (controls, n=21) and healthy elders instructed to simulate mild cognitive disorder (malingerers, n=21) were administered two background neuropsychological tests (MMSE, FAB) as well as the b Test.
Results: Malingerers performed significantly worse on all error scores as compared to patients and controls, and scored poorly than controls, but comparably to patients, on the time score. Patients scored significantly worse than controls on all scores, but both groups showed the same pattern of more omission than commission errors. By contrast, malingerers exhibited the opposite pattern with more commission errors than omission errors. Machine Learning models achieve an overall accuracy higher than 90% in distinguishing patients from malingerers on the basis of b Test results alone.
Conclusions: our findings suggest that b Test error scores accurately distinguish patients with Mild Neurocognitive Disorder from malingerers and may complement other validated procedures such as the Medical Symptom Validity Test.

Keywords: b Test, Malingering, Cognitive performance validity, Mild Cognitive Impairment, Italian population, Mild dementia

Received: 11 Feb 2019; Accepted: 01 Jul 2019.

Edited by:

Giovanni Pioggia, Italian National Research Council (CNR), Italy

Reviewed by:

Michelangelo Iannone, Italian National Research Council (CNR), Italy
Ben Schmand, University of Amsterdam, Netherlands  

Copyright: © 2019 Sartori, Pace, Orrù, Monaro, Gnoato, Vitaliani, Boone and Gemignani. 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. Giuseppe Sartori, University of Padova, Padova, Italy, giuseppe.sartori@unipd.it