One year of musical training affects development of auditory cortical-evoked fields in young children
Bibliographic record
Abstract
Auditory evoked responses to a violin tone and a noise-burst stimulus were recorded from 4- to 6-year-old children in four repeated measurements over a 1-year period using magnetoencephalography (MEG). Half of the subjects participated in musical lessons throughout the year; the other half had no music lessons. Auditory evoked magnetic fields showed prominent bilateral P100m, N250m, P320m and N450m peaks. Significant change in the peak latencies of all components except P100m was observed over time. Larger P100m and N450m amplitude as well as more rapid change of N250m amplitude and latency was associated with the violin rather than the noise stimuli. Larger P100m and P320m peak amplitudes in the left hemisphere than in the right are consistent with left-lateralized cortical development in this age group. A clear musical training effect was expressed in a larger and earlier N250m peak in the left hemisphere in response to the violin sound in musically trained children compared with untrained children. This difference coincided with pronounced morphological change in a time window between 100 and 400 ms, which was observed in musically trained children in response to violin stimuli only, whereas in untrained children a similar change was present regardless of stimulus type. This transition could be related to establishing a neural network associated with sound categorization and/or involuntary attention, which can be altered by music learning experience.
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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 | 0.002 |
| 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".