Using Time-Processing Skills to Predict Reading Abilities in Elementary School Children
Bibliographic record
Abstract
This article aims at examining the relationship between temporal skills and reading. According to Tallal, dyslexia may be linked to a global deficit in temporal processing, which would be detrimental for discrimination of phonemes, and thus impair reading acquisition. The temporal deficit hypothesis is not consensual, and the exact nature of the temporal deficits assumed to be associated with dyslexia remains unknown. The aim of the present experiment is to better define the temporal processes involved in reading. To do so, elementary school children from 1st to 6th grade with varied reading skills levels were recruited (from weak to very good readers). Each participant performed four temporal tasks, that is, gap detection, temporal order judgement, interval discrimination and interval reproduction; and each task was performed in two different conditions, i.e., with signals marking time delivered in the visual and in the auditory modalities. The results show positive correlations between reading skills and all temporal tasks, in both modalities. We also established a prediction model of reading skills with visual gap detection sensitivity as the best predictor. The results support Tallal’s theory. Temporal deficits in weak readers are global and transcend sensory modalities. The gap detection task in the visual modality shows clinical potential for identifying timing-related reading difficulties, and could be used in future research.
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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 | 0.001 | 0.004 |
| 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 | 0.000 |
| 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 | 0.001 |
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".