Maturation of the Mismatch Negativity: Effects of Profound Deafness and Cochlear Implant Use
Bibliographic record
Abstract
The use of cochlear implants to restore auditory sensation in deaf children is increasing, with a trend toward earlier implantation. However, little is known about how auditory deprivation and subsequent cochlear implant use affect the maturing human central auditory system. Our previous studies have demonstrated that the obligatory auditory evoked potentials (AEPs) of implanted children are very different from those of normal-hearing children. Unlike the obligatory potentials, which primarily reflect neural responses to stimulus onset, the mismatch negativity (MMN) provides a neurophysiological measure of auditory short-term memory and discrimination processes. The purpose of this investigation is to review our studies of the effects of auditory deprivation due to profound deafness and cochlear implant use on the maturation of the MMN in children, placed in the context of overall age-related changes in the AEPs. The development and application of a statistical technique to assess the MMN in individuals is also reviewed. Results show that although the morphology of the obligatory AEPs is substantially altered by the absence of a normal N(1) peak, the MMN is robustly present in a group of implanted children who have good spoken language perception through their device. Differences exist in the scalp distribution of the MMN between implanted and normal-hearing children. Specifically, the MMN appears to be more symmetrical in amplitude over both hemispheres, whereas it is initially much larger over the contralateral hemisphere in normal-hearing children. These findings suggest that, compared to N(1), the MMN is a better measure of basic auditory processes necessary for the development of spoken language perception skills in profoundly deaf children and adults who use a cochlear implant.
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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 | 0.001 | 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".