Bibliographic record
Abstract
The development of the human brain continues through to early adulthood. It has been suggested that cortical plasticity during this protracted period of development shapes circuits in associative transmodal regions of the brain. Here we considered how cortical plasticity during development might contribute to the coordinated brain activity required for speech motor learning. Specifically, we examined patterns of brain functional connectivity (FC), whose strength covaried with the capacity for speech audio-motor adaptation in children ages 5–12 and in young adults of both sexes. Children and adults showed distinct patterns of the encoding of learning in the brain. Adult performance was associated with connectivity in transmodal regions that integrate auditory and somatosensory information, whereas children rely on basic somatosensory and motor circuits. A progressive reliance on transmodal regions is consistent with human cortical development and suggests that human speech motor adaptation abilities are built on cortical remodeling, which is observable in late childhood and is stabilized in adults. SIGNIFICANCE STATEMENT A protracted period of neuro plasticity during human development is associated with extensive reorganization of associative cortex. We examined how the relationship between FC and speech motor learning capacity are reconfigured in conjunction with this cortical reorganization. Young adults and children aged 5–12 years showed distinctly different patterns. Mature brain networks related to learning included associative cortex, which integrates auditory and somatosensory feedback in speech, whereas the immature networks in children included motor regions of the brain. These patterns are consistent with the cortical reorganization that is initiated in late childhood. The result provides insights into the human biology of speech as well as to the mature neural mechanisms for multisensory integration in motor learning.
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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".