ArticleThe journals of gerontology. Series B, Psychological sciences and social sciences2025
Computational Phenotyping of Cognitive Decline With Retest Learning.
Article in The journals of gerontology. Series B, Psychological sciences and social sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The trial behind it
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Who cites it
4 citing papers in PubMed.
- Technology-natives and technology-adopters: Age differences in remote cognitive assessment in Alzheimer disease.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- Digital Assessment of Objective and Patient-Reported Cognition Across Migraine Phases: Results from the MIND Cohort.medRxiv : the preprint server for health sciences · 2026Article
- Digital Technology in Cognitive Decline: Bibliometric and Visualization Study.Current Alzheimer research · 2026Review
- Partially Observable Predictor Models for Identifying Cognitive Markers.Computational brain & behavior · 2025Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
objectivesCognitive 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 Alzheimer's Disease and Related Dementias (ADRD).
methodWe applied a computational cognitive model of learning and forgetting to data from Einstein Aging Study (EAS; 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.
resultsAsymptotic performance was related to both mild cognitive impairment (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.
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Registered trials
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