Adult age differences in the use of envelope cues to identify noise-vocoded words with a varying number of frequency bands
Bibliographic record
Abstract
Older adults with good audiograms have difficulty understanding speech in noise. Age-related differences have been found on some temporal processing measures such as gap detection; however, older adults are believed to have well-preserved ability to use envelope cues to identify words. Following Shannon et al. (1995), we used noise-vocoded speech such that the amplitude envelope of speech was retained in frequency bands but filled with noise, thereby obliterating fine structure cues within each band. In experiment 1, younger and older listeners heard a list of words. Each word was presented first with one vocoded frequency band, and the number of bands was incremented until the listener correctly identified the word. The average number of bands required for correct identification was found to be identical for both age groups. In experiment 2, both age groups identified words in four blocked noise-vocoded conditions (16, 8, 4, and 2 bands). Younger adults outperformed older adults. Although older adults were as able as younger adults to use envelope cues cumulatively in experiment 1, they were less able to use these cues without the benefit of repetition.
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".