Electrophysiological markers of early auditory temporal resolution deterioration with aging
Bibliographic record
Abstract
Aging significantly impacts auditory perception, particularly temporal resolution, which is the ability to detect rapid changes in sounds. Temporal resolution is often studied using gap detection methods, where a silent gap is inserted into a longer noise duration. Previous research indicates that aging negatively affects gap detection at the behavioral level. To objectively examine this process, electrophysiological correlates can be studied using a multi-deviant oddball paradigm. Existing data show reduced Deviant-Related Negativity (DRN) amplitudes and increased latencies in older adults compared to younger adults, suggesting a pre-attentive decline in processing basic temporal stimulus features. The effects of aging on gap detection have not been extensively investigated, making it uncertain whether changes can be detected in groups with smaller age differences. This study aims to investigate DRN and P2/P3a components in healthy, normal-hearing young (18-25 years) and middle-aged (30-45 years) adults using a multi-deviant paradigm combined with a behavioral gap audibility task with fixed gap durations. While behavioral task results were inconclusive, DRN latency analysis revealed significant differences during adaptation to deviant stimuli between age groups at various gap durations, displaying opposite slopes between both groups. Specifically, middle-aged participants exhibited increasing slopes, while younger participants showed decreasing slopes. Additionally, middle-aged adults exhibited significantly increased P2/P3a latencies compared to younger adults. These findings not only confirm the impact of aging on DRN during temporal resolution tasks but also suggest that examining the P2/P3a component could be valuable for early detection of age-related auditory decline.
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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 | 0.001 | 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) | 0.001 | 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".