Reading skill is related to individual differences in brain structure in college students
Bibliographic record
Abstract
We compared cortical anatomy, using 3D brain MRI scans, between three groups of university students: proficient readers (skilled at phonological decoding and text comprehension), poor readers (impaired at phonological decoding and text comprehension), and resilient readers (impaired at phonological decoding but skilled in text comprehension). This latter group provides a unique opportunity to investigate associations between cortical morphology and phonological decoding deficits in individuals without attendant reading comprehension deficits. We predicted widespread reductions in gray matter thickness and brain size in temporal and frontal regions in poor readers, and more focal differences in brain morphology in resilient readers. Typical asymmetry of gray matter thickness in the temporo-parietal region was reduced in both poor and resilient readers. Poor readers also exhibited smaller brain sizes in the right inferior frontal region than both proficient and resilient readers. Altered asymmetry in the temporo-parietal region may therefore be associated with poor phonological decoding and impaired text comprehension may be associated with altered frontal morphology. Resilient readers show relatively focal behavioral differences from typical readers, so it is interesting that they show reliable differences in brain morphology.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".