Beta-band modulation reveals the cortical dynamics of auditory statistical learning in children
Bibliographic record
Abstract
Children's ability to extract statistical regularities from speech is considered fundamental to lexical, syntactic, and grammatical development. However, the neural oscillatory mechanisms supporting this process in childhood remains poorly understood. While beta-band oscillations have been linked to statistical learning in visual and motor domains, it is unclear whether similar dynamics support auditory statistical learning in children. In this study, we recorded electroencephalography (EEG) from children aged 8-12 years as they listened to a continuous stream of trisyllabic nonwords (e.g., dapiku), where syllable order within each nonword was fixed (high predictability), but transitions between nonwords were variable (low predictability). Beta power was significantly lower for the more predictable second and third syllables relative to the less predictable first syllable. This effect emerged only after repeated exposure and was localised to left prefrontal electrodes. Beta power also correlated with post-exposure recognition accuracy. Additional learning-related modulations were observed in the theta-alpha and delta-theta bands, suggesting broader oscillatory engagement. These findings indicate that auditory statistical learning in middle childhood engages frequency-specific neural dynamics, with beta power modulations showing parallel effects to those observed in other modalities.
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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) | 0.002 | 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".