Predicting Cognitive Training Performance Using Cyclic Dual Latent Discovery Inindividuals with Mild Cognitive Impairment

This study investigated the utility of the cyclic dual latent discovery (CDLD) model inpredicting cognitive training performance among individuals with mild cognitive impairment(MCI), using data from the SUPERBRAIN-MEET randomized controlled trial. CDLDintegrates dual deep neural networks to model the latent traits of both users and trainingcontents, enabling the prediction of task accuracy prior to engagement. The model wastrained on 9,607 observations collected from 130 participants across 166 cognitive trainingtasks. CDLD demonstrated superior predictive accuracy compared to conventional modelsincluding random forest, gradient boosting, and matrix factorization, achieving a root meansquared error of 0.132 on the test set. Ablation analysis underscored the critical contributionof latent traits to prediction performance. Moreover, user latent traits showed significantassociations with baseline cognitive measures, particularly in visuospatial function andimmediate memory. These findings suggest that CDLD predicted training performance byeffectively capturing individual’s cognitive characteristics. By tailoring content withpredicted user performance, CDLD may optimize training efficacy and engagement inindividuals with MCI.