Speech comprehension and listening effort in noise: a comparison of younger and older adults
Bibliographic record
Abstract
BACKGROUND: Listening effort refers to the amount of processing resources deployed to understand speech. Using a dual-task paradigm this study investigated whether older adults expend more listening effort than younger adults when performing a speech comprehension task.METHODS: Four groups took part in this study: younger adults with normal-hearing, young normal-hearing adults who heard a low pass version of the speech material, older adults with a moderate to moderately severe hearing loss and older adults with age-related normal hearing. A dual task paradigm was used. The primary task consisted of listening to a short documentary heard in a background of a 4-talker speech babble. A questionnaire was used to measure speech comprehension. The secondary task consisted of a playing card sorting task. Each task was performed separately and concurrently.RESULTS: The older adults with age-related normal hearing deployed more listening effort than the two younger groups of younger adults. The performance of the older adults with a moderate to moderately severe hearing loss did not differ from the performance of the two groups of young adults nor from the group of older adults with age-related normal hearing.CONCLUSIONS: One possible explanation for these results is that individuals with a significant hearing loss naturally develop efficient speech compensation strategies to communicate. As a consequence, the use of these strategies becomes automatic, and their use requires less attentional and other cognitive resources than is the case for individuals with a lesser degree of hearing loss.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".