Preserved context sensitivity in language production: Lexical differentiation in older adults.
Bibliographic record
Abstract
Aging is often associated with cognitive decline, particularly in memory, which can impact language production. However, older adults (OA) do not exhibit a decline in crystallized intelligence, which reflects accumulated knowledge and expertise. The present study focuses on the referential phenomenon of lexical differentiation: When speakers refer to an object after earlier referring to a different exemplar from the same category, younger speakers sometimes use modified expressions (e.g., "the open umbrella") even though the earlier referent is no longer visible. We examine two hypotheses regarding lexical differentiation in older adults: the memory-based view that predicts less lexical differentiation in older adults due to memory decline, and the communication-based view that predicts equal or more lexical differentiation in older adults due to communicative and linguistic expertise. Results show that older adults produced similar levels of lexical differentiation (when considering all modifiers) and more lexical differentiation than younger adults (when focusing on prenominal modification), supporting the communication-based view. In addition, older adults produced more postnominal modifiers, which do not require early planning. These results highlight the adaptability of older adults in language production and provide new insights into how aging influences context-sensitive language use. (PsycInfo Database Record (c) 2026 APA, all rights reserved).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".