Effects of age, hearing loss, and linguistic complexity on listening effort as measured by working memory span
Bibliographic record
Abstract
Listeners, especially older or hard-of-hearing individuals, report that understanding conversation in noisy situations is effortful. Individual differences may be due to auditory and/or cognitive processing abilities. It is assumed that auditory processing depends on the individual’s degree and type of hearing loss, while cognitive processing depends on the individual’s working memory (WM) capacity. Many researchers have measured reading WM span to minimize the effects of hearing loss; however, variations due to hearing loss may be important in assessing processing demands (listening effort) during speech-in-noise understanding. The effects of the acoustical properties of the signal and masker on listening effort have been studied, but less is known about the effects of the linguistic complexity of the materials. We predicted that demands on WM and the correlation between WM span measures and speech-in-noise performance would increase with increasing linguistic complexity, with speech-in-noise performance correlating more strongly with auditory than with visual measures of WM span. To test these hypotheses, we administered speech tests varying in linguistic complexity and measures of both reading and listening WM span. Participants were a group of younger listeners with normal hearing and a group of older listeners with bilateral sensorineural 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 | 0.004 |
| 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 | 0.000 | 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".