{"doi":"10.1093/geronb/gbaf030","title":"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 Alzheimer's Disease and Related Dementias (ADRD). METHOD: We 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. RESULTS: Asymptotic 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.","journal":"The Journals of Gerontology Series B","year":2025,"id":530443,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":5,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.5507,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":501352,"name":"Joachim Vandekerckhove","orcid":"0000-0003-2600-5937","position":1,"is_corresponding":false},{"id":790993,"name":"Jonathan G. Hakun","orcid":"0000-0003-3389-7136","position":2,"is_corresponding":false},{"id":105012,"name":"Sharon H. Kim","orcid":null,"position":3,"is_corresponding":false},{"id":251473,"name":"Mindy J. Katz","orcid":"0009-0004-7524-0292","position":4,"is_corresponding":false},{"id":251474,"name":"Cuiling Wang","orcid":"0000-0001-7806-1928","position":5,"is_corresponding":false},{"id":234917,"name":"Richard B. Lipton","orcid":"0000-0003-2652-2897","position":6,"is_corresponding":false},{"id":388827,"name":"Carol A. Derby","orcid":"0000-0002-5657-4519","position":7,"is_corresponding":false},{"id":816912,"name":"Nelson Roque","orcid":"0000-0003-1184-202X","position":8,"is_corresponding":false},{"id":320838,"name":"Martin J. Sliwinski","orcid":"0000-0002-9611-7558","position":9,"is_corresponding":false},{"id":255676,"name":"Zita Oravecz","orcid":"0000-0002-9070-3329","position":0,"is_corresponding":true}],"reference_count":47,"raw_metadata":null,"created_at":"2026-07-19T02:51:05.836974Z","pmid":"39964977","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}