Reading skill related to left ventral occipitotemporal cortex during a phonological awareness task in 5–6-year old children
Bibliographic record
Abstract
The left ventral occipitotemporal cortex (vOT) is important in visual word recognition. Studies have shown that the left vOT is generally observed to be involved in spoken language processing in skilled readers, suggesting automatic access to corresponding orthographic information. However, little is known about where and how the left vOT is involved in the spoken language processing of young children with emerging reading ability. In order to answer this question, we examined the relation of reading ability in 5-6-year-old kindergarteners to the activation of vOT during an auditory phonological awareness task. Two experimental conditions: onset word pairs that shared the first phoneme and rhyme word pairs that shared the final biphone/triphone, were compared to allow a measurement of vOT's activation to small (i.e., onsets) and large grain sizes (i.e., rhymes). We found that higher reading ability was associated with better accuracy of the onset, but not the rhyme, condition. In addition, higher reading ability was only associated with greater sensitivity in the posterior left vOT for the contrast of the onset versus rhyme condition. These results suggest that acquisition of reading results in greater specialization of the posterior vOT to smaller rather than larger grain sizes in young children.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".