Older Adults Expend More Listening Effort Than Young Adults Recognizing Speech in Noise
Bibliographic record
Abstract
PURPOSE: Listening in noisy situations is a challenging experience for many older adults. The authors hypothesized that older adults exert more listening effort compared with young adults. Listening effort involves the attention and cognitive resources required to understand speech. The purpose was (a) to quantify the amount of listening effort that young and older adults expend when they listen to speech in noise and (b) to examine the relationship between self-reported listening effort and objective measures. METHOD: A dual-task paradigm was used to objectively evaluate the listening effort of 25 young and 25 older adults. The primary task involved a closed-set sentence-recognition test, and the secondary task involved a vibrotactile pattern recognition test. Participants performed each task separately and concurrently under 2 experimental conditions: (a) when the level of noise was the same and (b) when baseline word recognition performance did not differ between groups. RESULTS: Older adults expended more listening effort than young adults under both experimental conditions. Subjective estimates of listening effort did not correlate with any of the objective dual-task measures. CONCLUSIONS: Older adults require more processing resources to understand speech in noise. Dual-task measures and subjective ratings tap different aspects of listening effort.
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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".