Rhythm as an independent determinant of developmental dyslexia.
Bibliographic record
Abstract
Temporal accounts of Developmental Dyslexia (DD) postulate that a timing impairment plays an important role in this learning disorder. However, DD has been associated with timing disorders as well as other motor and cognitive dysfunctions. It is still unclear whether nonverbal timing skills per se may be considered as independent determinants of DD. In this study, we investigated the independent contribution of predictive timing to DD above and beyond the motor and cognitive dysfunctions typically associated with this disorder. Twenty-one children with DD (aged 8-12, nine females) and 27 controls (14 females) were evaluated on perceptual timing, finger tapping, fine motor control, as well as attention and executive tasks. Participants were native French speakers from various socioeconomic backgrounds. The performance of children with DD was poorer than that of controls in most of the tasks. Predictors of DD, as identified by logistic regression modeling, were beat perception and precision in tapping to the beat, which are both predictive timing variables, children's tapping rate, and cognitive flexibility. These data support temporal accounts of DD in which predictive timing impairments partially explain the core phonological deficit, independent from general motor and cognitive functioning, making predictive timing a valuable tool for early diagnosis and remediation of DD. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".