Age‐related changes in transient and oscillatory brain responses to auditory stimulation during early adolescence
Bibliographic record
Abstract
Maturational changes in the capacity to process quickly the temporal envelope of sound have been linked to language abilities in typically developing individuals. As part of a longitudinal study of brain maturation and cognitive development during adolescence, we employed dense-array EEG and spatiotemporal source analysis to characterize maturational changes in the timing of brain responses to temporal variations in sound. We found significant changes in the brain responses compared longitudinally at two time points in early adolescence, namely 10 years (65 subjects) and 11.5 years (60 of the 65 subjects), as well as large differences between adults, studied with the same protocol (Poulsen, Picton & Paus, 2007), and the children at 10 and 11.5 years of age. The transient auditory evoked potential to tone onset showed decreases in the latency of vertex and T-complex components, and a highly significant increase in the amplitude of the N1 wave with increasing age. The auditory steady state response to a 40-Hz frequency-modulated tone increased in amplitude with increasing age. The peak frequency of the envelope-following response to sweeps of amplitude-modulated white noise also increased significantly with increasing age. These results indicate persistent maturation of the cortical mechanisms for auditory processing from childhood into middle adulthood.
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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".