Making young ears old and old ears even older: Simulating a loss of synchrony
Bibliographic record
Abstract
Age-related changes in the auditory system have been attributed to three independent factors: OHC damage, changes in endocochlear potentials, and loss of neural synchrony. In previous studies, a jitter algorithm has been used to simulate the loss of synchrony in young adults (MacDonald et al., 2005). In this study, the effect of jitter on old adults with good audiograms in the speech range is explored. SPIN-R sentences were presented in two SNR and three processing conditions: intact, jitter, and smear. The parameters of the jittering algorithm were the same as those used with young adults. The parameters of smearing algorithm were chosen to match the spectral distortion produced by jitter algorithm. While both the jitter and smear conditions resulted in a significant decline in word identification, the decline was largest in the jitter condition. Psychometric functions were fitted to the data and compared to previous work with young adults. The comparison supports the hypothesis that loss of synchrony can adversely affect speech intelligibility in noise, and is consistent with the hypothesis that loss of synchrony occurs with age. As well, the comparison suggests that the effect of jitter may be linear.
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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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".