Features of learning from high-frequency cognitive assessments as digital biomarkers of cognitive change
Innovation in Aging(2023)
摘要
Abstract In measurement burst designs, participants’ cognitive performance is measured multiple times per day, for several days, forming a measurement burst. Ideally, these are repeated once or twice a year as people age. Such rich longitudinal data are generated by multiple processes (e.g., aging and learning) that operate on multiple timescales. We propose a Bayesian process model that can extract person-specific, substantively meaningful features of learning and change from such data. We show how to model retest gains across measurement bursts, as well as warm-up effects within a burst, while quantifying change across bursts in peak performance and accounting for short-term within-person variability. Individual differences in these features are also linked with psychosocial variables and biomarkers of cognitive decline in a one-step analysis. We also highlight how this approach allows for drawing intuitive inferences on cognitive decline with Bayesian posterior probabilities.
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