Difference Between Children and Adults in the Print-speech Coactivated Network
Bibliographic record
Abstract
Skilled reading requires the convergent brain network between spoken language and reading. Here, we examined the developmental changes in this convergent network between beginning and skilled readers. We used fMRI data from 41 adults (aged from 20 to 21 years) and 21 children (aged from 9 to 12 years) who performed a Chinese word sound matching task in speech and print. Three complementary analytic approaches, including print-speech conjunction analysis, brain-behavioral analysis, and functional connectivity analysis were performed to reveal the link between print and speech neural systems as well as the developmental changes in this link. We found that (1) adults showed broader convergence than children; (2) coactivation in anterior dorsal regions (i.e., inferior frontal gyrus and insula) predominated in children, while coactivation in the ventral posterior region (i.e., middle temporal gyrus) predominated in adults, which aligns with the dual-stream model of reading; and (3) print-speech convergence regions showed task-specific functional connectivity patterns and there was an increased visual-modal specialization in the functional connectivity patterns with reading development. Our study provides important evidence for developmentally increased coactivation in ventral posterior regions and increased task specialization in print-speech convergence regions, which is crucial for successful reading.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".