Detecting Response Bias on the MindStreams Battery

Abstract

The most effective tests for detection of negative response bias (NRB) are based on the forced choice paradigm, but patients coached prior to their administration may easily discern their purpose. Algorithms for NRB detection based upon parameters from standard neuropsychological tests may be more difficult for patients to discern and thus more effective in detecting NRB. The current study aimed to uncover abnormal performance patterns associated with NRB on the MindStreams computerized battery. Simulators, cognitively healthy controls, and genuinely cognitively impaired patients participated in the study. A repeated measures analysis was applied to delineate anomalous patterns on Verbal Memory, Non-Verbal Memory, Staged Information Processing Speed, and Stroop tests. The variables derived from this analysis were entered into a multivariate discriminant function analysis (DFA). The DFA model, based on accuracy, reaction time (RT) and accuracy/RT measures, differentiated efficiently between simulators and the two control groups. Various cut-offs were provided based on this model. It is concluded that computerized cognitive testing may be valuable for malingering assessment using detection strategies based on performance patterns.

Key words:

• attention
• CVA
• executive functions
• information processing
• malingering
• MindStreams
• neuropsychological assessment
• NeuroTrax
• response bias
• TBI

Acknowledgments

We wish to thank Doron Fiala, and Amir Tzandekovich for their assistance in collecting the data for the cognitively impaired group.

Additional information

Notes on contributors

Omer Hegedish

Some of the findings included here were previously presented by the authors at the Annual Conference of the Israeli Neuropsychological Society, 2009.Conflict of interest: Doniger is a current employee and Schweiger a former employee of NeuroTrax Corporation, the publisher of MindStreams.