Social network indices impact spoken word recognition across the adult lifespan
Bibliographic record
Abstract
Social engagement is critical for cognitive well-being in older adulthood, but little is known about the relationship between the mechanisms of language processing and social networks as listeners age. In young, normal-hearing listeners diverse social networks are known to support more flexible speech processing (Kutlu et al., 2024). The competition dynamics of word recognition change with age, even in people with normal hearing (Colby and McMurray, 2023). Thus, we ask whether large, diverse social networks can bolster language processing in older adulthood. The current study tested a large group of adults (N = 76, 30–80 years old) on a Visual World Paradigm task to assess the real-time competition dynamics underlying spoken word recognition and a Social Network Questionnaire as a measure of the quality and quantity of their social engagement. Data collection is ongoing, but preliminary analyses suggest listeners with more dense social networks activate words faster, and those who regularly converse with more individuals better manage competition between words. This work confirms the importance of maintaining social bonds in older adulthood.
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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 | 0.000 | 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) | 0.004 | 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".