Everyday Language and Cognitive Functioning in Late Life

Bibliographic Data

ID6457706
AuthorsShiyang Zhang (0000-0001-6229-3652, Population Research Center, The University of Texas at Austin , Austin, Texas), Zexi Zhou (0000-0002-7817-724X, The University of Texas at Austin), Yee To Ng (0000-0002-6757-6835, University of Michigan), Emilio Muñoz (0000-0003-2152-5917, Population Research Center, The University of Texas at Austin , Austin, Texas), Junyi Jessy Li (0000-0002-2550-5262, The University of Texas at Austin), K L Fingerman (0000-0001-9260-9400, Population Research Center, The University of Texas at Austin , Austin, Texas)
EditorsLaura Germine (0000-0001-8690-8412, (Psychological Sciences Section)), Laura Thi Germine ((Psychological Sciences Section))
Year2025
Volume80
Issue7
Publication date2025-05-02
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueThe Journals of Gerontology Series B (JOURNAL)
Journal identifiersISSN: 1079-5014 • E-ISSN: 1758-5368
PublisherOxford University Press (OUP) (PUBLISHER)
DOI10.1093/geronb/gbaf084
PMID40326509
OpenAlexW4410105623
LanguageEN
Citations received1
References cited21

Objectives Language 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. Method Participants 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. Results Principal 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

Cognition · Cognitive psychology · Cognitive skill · Developmental psychology · Natural language processing · Recall · Sentence · Computer Science · Dementia and Cognitive Impairment Research · Neurobiology of Language and Bilingualism · Psychology

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