Related Experiment Video
Updated: Apr 28, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
A Bayesian latent group analysis for detecting poor effort in a sample of cognitively impaired patients
Alonso Ortega1, Martina Piefke, Hans J Markowitsch
1a Physiological Psychology , Bielefeld University , Bielefeld , Germany.
Abstract:
Using a Bayesian latent group analysis in a simulation design, we recently showed a high diagnostic accuracy when assessing effort in the context of malingered memory deficits. We here further evaluate our Bayesian model in a sample of cognitively impaired patients. The main analysis showed both high sensitivity and specificity, thus corroborating a high diagnostic accuracy of the model. Additional analysis showed variations on effort estimates after changes in malingering base rates. Variations affected sensitivity, but not specificity, which is in line with typical findings in malingering research. These data suggest that Bayesian analyses may complement and improve existing effort measures.

