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Updated: Jul 11, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Identification of feigned mental retardation using the new generation of malingering detection instruments:
Lili O Graue1, David T R Berry, Jessica A Clark
1University of Kentucky, Lexington, KY 40506, USA.
Abstract:
A recent Supreme Court decision--Atkins v. Virginia, 536 U.S. 304 (2002)--prohibiting the execution of mentally retarded (MR) defendants may have raised the attractiveness of feigning this condition in the criminal justice system. Unfortunately, very few published studies have addressed the detection of feigned MR. The present report compared results from tests of intelligence, psychiatric feigning, and neurocognitive faking in a group of 26 mild MR participants (MR) and 25 demographically matched community volunteers asked to feign MR (CVM). Results showed that the CVM suppressed their IQ scores to approximate closely the level of MR participants. WAIS-III and psychiatric malingering measures were relatively ineffective at discriminating feigned from genuine MR. Although neurocognitive malingering tests were more accurate, their reduced specificity in MR participants was of potential concern. Revised cutting scores, set to maintain a Specificity rate of about .95 in MR clients, were identified, although they require cross-validation. Overall, these results suggest that new cutting scores will likely need to be validated to detect feigned MR using current malingering instruments.
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