Evidence map›Paper›PMID 40326509›Full record

ArticleThe journals of gerontology. Series B, Psychological sciences and social sciences2025

Everyday Language and Cognitive Functioning in Late Life.

Shiyang Zhang, Zexi Zhou, Yee To Ng, Elizabeth Muñoz, Junyi Jessy Li, Karen Fingerman

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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. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Shiyang ZhangPopulation Research Center, The University of Texas at Austin, Austin, Texas, USA.ORCID 0000-0002-0530-0163
Zexi ZhouDepartment of Human Development and Family Sciences, The University of Texas at Austin, Austin, Texas, USA.ORCID 0000-0002-7817-724X
Yee To NgDepartment of Physical Medicine and Rehabilitation, University of Michigan, Ann Arbor, Michigan, USA.ORCID 0000-0002-6757-6835
Elizabeth MuñozPopulation Research Center, The University of Texas at Austin, Austin, Texas, USA.ORCID 0000-0003-2152-5917
Junyi Jessy LiDepartment of Linguistics, The University of Texas at Austin, Austin, Texas, USA.ORCID 0000-0002-2550-5262
Karen FingermanPopulation Research Center, The University of Texas at Austin, Austin, Texas, USA.ORCID 0000-0001-9260-9400

Funding

Program Development and Pilot CoreP30AG066614 · NIA · UNIVERSITY OF TEXAS AT AUSTIN · PI KAREN L FINGERMAN · 2020 to 2026
$5.2M
Social Networks and Well-being in Late Life: A Study of Daily MechanismsR01AG046460 · NIA · UNIVERSITY OF TEXAS AT AUSTIN · PI FINGERMAN, KAREN L · 2015 to 2019
$2.5M
Center on Aging and Population SciencesEunice Kennedy Shriver National Institute of Child Health and Human Development P2CHD042849NIA NIH HHS P30AG066614NIA NIH HHS R01AG046460NIH HHSPopulation Research Center at The University of Texas at AustinUniversity of Texas at Austin
6 · The paper itself

Abstract

objectivesLanguage deterioration is a marker of cognitive decline in late life. An emerging literature has examined features of language associated with executive functioning and working memory when older adults are cognitively healthy. This study aims to identify linguistic features that predict cognitive functioning in a sample of cognitively healthy individuals.

methodParticipants from the Daily Experiences and Well-being Study (DEWS; aged 65-89, N = 260) completed a battery of standard cognitive tests. They wore an Android device containing the Electronically Activated Recorder (EAR) app, which recorded ambient sound 30 seconds every 7 min for 5-6 days (N = 26,339 sound files with participant speech). Linguistic Inquiry and Word Count (LIWC) software generated linguistic features from transcriptions of recorded speech. Machine learning models (random forest classifier) were trained with the linguistic features (n = 29) to predict cognitive functioning.

resultsPrincipal component analysis revealed that the cognitive domains fit a single factor. The random forest classifier achieved robust model performance (accuracy = 0.72, precision = 0.74, recall = 0.91, F1-score = 0.81, and area under curve = 0.73). Linguistic features most strongly associated with cognitive functioning included: first-person singular pronouns (with worse cognitive functioning), articles, words indicating differentiation, first-person plural pronouns, and words per sentence (with better cognitive functioning). DISCUSSION: Findings suggest that language processes are evident across multiple domains of cognitive functioning when older adults remain within a cognitively healthy range. Use of complex language may indicate optimal cognitive functioning, a topic worthy of future investigation.

Indexed as

AgingCognitionCognitive DysfunctionExecutive FunctionLanguageAgedAged, 80 and overFemaleHumansMachine LearningMaleMemory, Short-TermNeuropsychological TestsCognitive agingEcological momentary assessmentElectronically activated recorder

Identifiers

PMID40326509
PMCPMC12257087

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.