A Neuroeducational Approach to Defining the Cognitive Profile of Comorbid Math and Reading Disabilities
Bibliographic record
Abstract
Psychoeducational studies have shown that the comorbidity between reading disability and mathematical disability is relatively high but the neuroanatomical substrates that may underlie this comorbidity have not been reliably identified.We developed a novel neuroeducational approach to bridge the corresponding concepts on learning disabilities in the neuropsychological and psychoeducational fields.First, we assessed the cognitive profiles of individuals with reading, mathematical and both disabilities (comorbid group), using psychoeducational tests also often used in neuropsychological test batteries.Second, we performed a systematic review of current literature on the neuroanatomical substrates of dyslexia and dyscalculia.Third, we mapped the cognitive profiles to the neuroanatomical substrates plausibly shared with dyslexia and dyscalculia.The comorbid group exhibited reading deficits similar to those shown by individuals having reading disability alone, which may be associated with atypical function at the left inferior frontal and left fusiform gyri similar to the mathdisabled group; they also exhibited deficits in quantitative reasoning, which may be associated with a bilateral atypical function of the intraparietal sulci.Further deficits related to verbal working memory and semantic memory were exclusive to the comorbid group.The current approach suggests that impaired phonological, numerical, semantic, and working memory processes may be associated with atypical function of the left angular gyrus in both reading and mathematical disabilities.iii I'd like to thank my fellow students in the NICER Lab, Matt Buchanan, Nina Hedayati, and Faisa Omer.Matt, thank you for the all the opportunities you've provided me with, and for always encouraging me to get more skills and develop as a neuropsychologist.Nina, thank you for sharing your time and space with me on so many occasions, and enduring the struggle of application deadlines and thesis milestones alongside me.Faisa, thanks for keeping it 100% real with me and always having my back!You bring so much life and energy to the lab and I'm grateful for our friendship.I'd also like to thank
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".