Within- and Between- Channel Gaps Elicit Mismatch Negativity in the Aging Brain
Bibliographic record
Abstract
This study examined how older and younger adults process silent gaps in auditory stimuli by recording cortical evoked potentials using a multi-deviant paradigm that is compared to a psychophysical gap detection task. Participants passively listened to pairs of noise markers separated by silent intervals. Markers were either spectrally identical (within-channel) or spectrally distinct (between-channel) narrowband noises. Seven gap durations served as deviants in a multi-deviant sequence. The deviance-related negativity (DRN) and the P2/P3a were recorded from fronto-central electrodes. Thirty-two participants with normal hearing or minimal hearing loss participated in this study. They were separated into an older adult (mean age = 63 years) and younger adult (mean age = 24 years) group. Gapped deviants elicited DRN in both within- and between-channel conditions. Age effects emerged in the DRN and peak-to-peak DRN-P2/P3a measure. Older adults showed longer DRN latencies and reduced amplitudes compared to the younger group. Condition effects showed contrasting DRN latency patterns between groups. P2/P3a responses alone did not show any condition or age-specific effects. Behavioral gap detection thresholds did not differ across conditions in older adults. Overall, results demonstrate that electrophysiological indices reveal subtle neural alterations in temporal resolution that may precede behavioral 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 | 0.000 | 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) | 0.001 | 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".