<HashMap><database>biostudies-literature</database><scores/><additional><omics_type>Unknown</omics_type><volume>9</volume><submitter>Cain E</submitter><pubmed_abstract>Humans learn the meanings of words from the contexts in which they are used. Patterns of language use change over time, suggesting that the contexts in which some words are experienced change across an individual's lifespan. Here, we investigated whether language users' semantic space changes in lockstep with changes in the language or whether it retains traces of historical language use/meanings. In two studies, we used distributional semantic word embeddings trained on corpora from different decades (HistWords) to capture meaning change at the level of the (English) language. We first compared these diachronic semantic spaces to the semantic spaces of individuals in different age cohorts (ranging from people in their 20s to people over 70) using an open dataset of associations norms (Sma</pubmed_abstract><journal>Open mind : discoveries in cognitive science</journal><pagination>2114-2148</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC12768554</full_dataset_link><repository>biostudies-literature</repository><pubmed_title>Semantic Representations Are Updated Across the Lifespan Reflecting Diachronic Language Change.</pubmed_title><pmcid>PMC12768554</pmcid><pubmed_authors>Ryskin R</pubmed_authors><pubmed_authors>Cain E</pubmed_authors></additional><is_claimable>false</is_claimable><name>Semantic Representations Are Updated Across the Lifespan Reflecting Diachronic Language Change.</name><description>Humans learn the meanings of words from the contexts in which they are used. Patterns of language use change over time, suggesting that the contexts in which some words are experienced change across an individual's lifespan. Here, we investigated whether language users' semantic space changes in lockstep with changes in the language or whether it retains traces of historical language use/meanings. In two studies, we used distributional semantic word embeddings trained on corpora from different decades (HistWords) to capture meaning change at the level of the (English) language. We first compared these diachronic semantic spaces to the semantic spaces of individuals in different age cohorts (ranging from people in their 20s to people over 70) using an open dataset of associations norms (Sma</description><dates><release>2025-01-01T00:00:00Z</release><publication>2025</publication><modification>2026-06-06T11:05:28.086Z</modification><creation>2026-05-29T03:13:01.17Z</creation></dates><accession>S-EPMC12768554</accession><cross_references><pubmed>41497527</pubmed><doi>10.1162/OPMI.a.315</doi></cross_references></HashMap>