Learning to Read Strengthens Functional Connectivity Between the Ventral Occipitotemporal Cortex and the Superior Temporal Gyrus During an Auditory Phonological Awareness Task
Bibliographic record
Abstract
PURPOSE: It is often assumed that phonological awareness only reflects children's phonological skill. However, orthographic representations have been found to be automatically involved during phonological awareness tasks, which we refer to as automatic orthographic activation. Although previous longitudinal neural studies have addressed how phonological processing during phonological awareness tasks is bidirectionally related to reading skill in developing children, we do not know how automatic orthographic activation plays a role in reading skill. METHOD: To address this gap, we measured 40 children's reading skill and brain activity during an auditory phonological task at two time points using functional magnetic resonance imaging. Children were 5.5 to 6.5 years old at the first time point and were followed up approximately 1.5 years later when they were 7 to 8 years old. RESULTS: We found that earlier reading skill predicted children's later functional connectivity during onset processing between the left superior temporal gyrus, a phonological region, and the left posterior ventral occipitotemporal cortex, an orthographic region representing letters. CONCLUSION: This finding, together with previous studies, suggests that learning to read influences phonological awareness not only by refining phonological representations but also via strengthening the automatic mapping between phonemes and letters during spoken language processing.
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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".