Letter fluency in 7-8-year-old children is related to the anterior, but not posterior, ventral occipito-temporal cortex during an auditory phonological task
Bibliographic record
Abstract
Previous studies have shown that reading skill in 3- to 6-year-old children is related to the automatic activation of the posterior left ventral occipitotemporal cortex (vOT) during spoken language processing, whereas 8- to 15-year-old children and adult readers activate the anterior vOT. However, it is unknown how children who are between these two age groups automatically activate orthographic representations in vOT for spoken language. In the current study, we recruited 153 7- to 8-year-old children to fill the age gap from previous studies. Using functional magnetic resonance imaging (fMRI), we measured children's reading-related skills and brain activity during an auditory phonological task with both a small (i.e. onset) and a large (i.e. rhyme) grain size condition. We found that letter fluency, but not reading accuracy, was correlated with activation in the anterior vOT for the rhyme condition. There were no reading-related skill correlations for the posterior vOT or for activation during the onset condition in this age group. Our findings reveal that automatic activation in the anterior vOT during spoken language processing already occurs in higher skilled 7- to 8-year-old children. In addition, increases in naming automaticity is the primary determinant of the engagement of vOT during phonological awareness tasks.
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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 | 0.000 |
| 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".