A neuroimaging dataset on orthographic, phonological and semantic word processing in school-aged children
Bibliographic record
Abstract
Here we describe the public neuroimaging and behavioral dataset entitled "Cross-Sectional Multidomain Lexical Processing" available on the OpenNeuro project (https://openneuro.org). This dataset explores the neural mechanisms and development of lexical processing through task based functional magnetic resonance imaging (fMRI) of rhyming, spelling, and semantic judgement tasks in both the auditory and visual modalities. Each task employed varying degrees of trial difficulty, including conflicting versus non-conflicting orthography-phonology pairs (e.g. harm - warm, wall - tall) in the rhyming and spelling tasks as well as high versus low word pair association in the semantic tasks (e.g. dog - cat, dish - plate). In addition, this dataset contains scores from a battery of standardized psychoeducational assessments allowing for future analyses of brain-behavior relations. Data were collected from a cross-sectional sample of 91 typically developing children aged 8.7- to 15.5- years old. The cross-sectional design employed in this dataset as well as the inclusion of multiple measures of lexical processing in varying difficulties and modalities allows for multiple avenues of future research on reading development.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.007 |
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".