Computational phenotyping of cognitive decline with retest learning

Abstract

OBJECTIVES: Cognitive change is a complex phenomenon encompassing both retest-related performance gains and potential cognitive decline. Disentangling these dynamics is necessary for effective tracking of subtle cognitive change and risk factors for ADRD. METHOD: We applied a computational cognitive model of learning and forgetting to data from Einstein Aging Study (n = 316). EAS participants completed multiple bursts of ultra-brief, high-frequency cognitive assessments on smartphones. Analyzing response time data from a measure of visual short-term working memory, the Color Shapes task, and from a measure of processing speed, the Symbol Search task, we extracted several key cognitive markers: short-term intraindividual variability in performance, within-burst retest learning and asymptotic (peak) performance, across-burst change in asymptote and forgetting of retest gains. RESULTS: Asymptotic performance was related to both MCI and age, and there was evidence of asymptotic slowing over time. Long-term forgetting, learning rate, and within-person variability uniquely signified MCI, irrespective of age. DISCUSSION: Computational cognitive markers hold promise as sensitive and specific indicators of preclinical cognitive change, aiding risk identification and targeted interventions.

Citation

Oravecz, Z., Vandekerckhove, J., Hakun, J., Kim, S., Katz, M., Wang, C., Lipton, R. B., Derby, C. A., Roque, N. A., & Sliwinski, M. (preprint). Computational phenotyping of cognitive decline with retest learning. PsyArXiv.

Bibtex

@article{oravecz_etal:preprint:Computational,
    title   = {{C}omputational phenotyping of cognitive decline with retest learning},
    author  = {Oravecz, Zita and Vandekerckhove, Joachim and Hakun, Jonathan and Kim, Sharon and Katz, Mindy and Wang, Cuiling and Lipton, Richard B. and Derby, Carol A. and Roque, Nelson A. and Sliwinski, Martin},
    year    = {preprint},
    journal = {PsyArXiv}
}