Cerebellar Tasks do not Distinguish Between Children with Developmental Dyslexia and Children with Intellectual Disability
Bibliographic record
Abstract
This paper explored the claim that only children with developmental dyslexia, whose reading ability is discrepant from their average general reasoning ability show specific deficits in motor tasks assessing cerebellar functioning (Fawcett et al., 2001 Fawcett, A. J. and Nicolson, R. I. 2001. “Dyslexia: The role of the cerebellum”. In Dyslexia: Theory and good practice, Edited by: Fawcett, A. J. 89–105. London: Whurr. [Google Scholar], Cerebellar tests differentiate between groups of poor readers with and without IQ discrepancy. J. Learning Disabilities, 34, 119) and rapid serial naming (RAN, Wolf & Bowers, 1999 Wolf, M. and Bowers, P. 1999. The double deficit hypothesis for the developmental dyslexias. Journal of Educational Psychology, 91: 1–24. [Crossref], [Web of Science ®] , [Google Scholar], The double deficit hypothesis for the developmental dyslexias. J. Educ. Psychol., 91, 1). All available children between the ages of 11 and 14 were recruited from two special schools for children with either (a) formally-diagnosed intellectual disabilities (N = 18); or (b) formal diagnoses of developmental dyslexia (N = 25). These two groups of children did not differ on gender, age, pseudoword decoding abilities, or on 7 of 8 literacy measures, but did differ significantly, as expected on verbal and non-verbal reasoning tasks. Importantly, there were no deficits in bead threading ability or postural stability in the children with developmental dyslexia compared to the children with intellectual disabilities. There were also no between-group differences in rapid naming measures. The present results therefore provide no support for the claim that cerebellar deficits or RAN distinguish between children with dyslexia and children with intellectual disabilities that include reading.
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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".