Taking the Extra Listening Mile: Processing Spoken Semantic Context Is More Effortful for Older Than Young Adults
Bibliographic record
Abstract
OBJECTIVES: Older adults use semantic context to generate predictions in speech processing, compensating for aging-related sensory and cognitive changes. This study aimed to gauge aging-related changes in effort exertion related to context use. DESIGN: The study revisited data from Harel-Arbeli et al. (2023) that used a "visual-world" eye-tracking paradigm. Data on efficiency of context use (response latency and the probability to gaze at the target before hearing it) and effort exertion (pupil dilation) were extracted from a subset of 14 young adults (21 to 27 years old) and 13 older adults (65 to 79 years old). RESULTS: Both age groups showed a similar pattern of context benefits for response latency and target word predictions, however only the older adults group showed overall increased pupil dilation when listening to context sentences. CONCLUSIONS: Older adults' efficient use of spoken semantic context appears to come at a cost of increased effort exertion.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".