Neurocognitive risk factors for co-occurring math difficulties in dyslexia: Differences in executive function and visuospatial processing
Bibliographic record
Abstract
What makes math difficulties so common in children with dyslexia? The current study aimed to identify behavioral and neurocognitive factors associated with co-occurring reading disability (RD) and math disability (MD). We tested reading, math, and cognitive skills in a sample of 86 children in 3rd–7th grade (ages 9-13) with RD. Within this sample, 35% of children had RD only with no weakness in math, 43% had co-occurring RD+MD, and over 20% demonstrated a possible vulnerability in math. We investigated whether RD-Only and RD+MD students differed behaviorally in their phonological awareness, reading skills, or executive function, as well as in the brain mechanisms underlying word reading and visuospatial working memory using fMRI. We found that the additional difficulty with math in children with RD was unrelated to differences in behavioral or brain measures of phonological awareness related to speech or print. However, the RD+MD group performed significantly worse than the RD-Only group on multiple measures of executive function, including working memory and processing speed. The RD+MD group also exhibited reduced brain activations for visuospatial working memory relative to the RD-Only group. Continuous analyses along a spectrum of math ability revealed that greater math difficulties were associated with reduced activation in the visual cortex. These converging neuro-behavioral findings suggest that poor executive function in general, and differences in visuospatial working memory in particular, are associated with co-occurring MD among children with RD.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".