Maturation in auditory event‐related potentials explains variation in language ability in children
Bibliographic record
Abstract
Processing of auditory information in the cortex continues to develop into later childhood and adolescence. Recent research has indicated that intraclass correlation (ICC) is the best method for capturing maturation in auditory event-related potentials (AEPs) of school-age children. However, the sensitivity of the ICC approach in discerning AEP changes in children has not been consistently demonstrated and positive results have not been replicated. We attempted this replication and further explored whether AEP maturation estimated using the ICC approach predicts cognitive and linguistic abilities in addition to chronological age. We measured AEPs in response to simple tones in groups of 7-, 8-, 9- and 10-year olds with typical development (N = 67) and used ICC to estimate the age equivalent of each child's AEP (AEP-age). Results indicated that ICC differentiated 7- to 8-year-old children from 9- to 10-year-old children and that AEP-age predicted both chronological age and significant, unique variance in language ability, but not in nonverbal IQ. Our findings support the view that auditory organization in children reflects both general developmental maturation and more specific development of language skills, and support the future use of AEP-age to identify and understand individual differences in brain maturation in typically developing and clinical populations.
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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 | 0.006 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".