Comprehension of Speeded Discourse by Younger and Older Listeners
Bibliographic record
Abstract
Researchers have argued that older adults are more adversely affected by speeding speech than are younger adults. However, the age effects usually occur when (1) the speech materials are artificially speeded to rates well above those that occur in natural speech; (2) the speeding method introduces distortions that tax the older adult's auditory processes; and (3) the speech materials are simple sentences or very short passages. This study evaluated whether older adults are disadvantaged when listening to extended discourse (10- to 15-min lectures) speeded to a rate near to the limit of normally encountered fast speech (240 words/min) with a minimum of acoustic distortion. Perceptual difficulty was further manipulated by presenting stimuli in either quiet or with a 12-talker background babble. Younger and older adults had more difficulty recalling the details of the discourse and integrating their contexts when stimuli were presented at faster rates and in higher levels of background noise. Although each of these manipulations were found to cause large differences in performance, the age groups were generally found to perform analogously in most conditions. Potentially the availability of semantically rich materials, and the extended durations of the passages, allowed the older adults an opportunity to adjust to the faster speech rates and maintain performance levels similar to younger adults.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.004 | 0.001 |
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".