Psychophysical Indexes of Temporal Processing Abnormalities in Children With Developmental Dyslexia
Bibliographic record
Abstract
Children with dyslexia and children progressing normally in reading performed several perceptual tasks to determine (a) the psychophysical measures that best differentiate children with dyslexia from children with average reading abilities; (b) the extent of temporal processing deficits in a single, well-defined group of children with dyslexia; and (c) the co-occurrence of visual and auditory temporal processing deficits in children with dyslexia. 4 of our 12 psychophysical tasks indicated differences in temporal processing ability between children with dyslexia and children with good reading skills. These included 2 auditory tasks (dichotic pitch perception and FM tone discrimination) and 2 visual tasks (global motion perception and contrast sensitivity). The battery of 12 tasks successfully classified 80% of the children into their respective reading-level groups. Within the group of children with dyslexia who had temporal processing deficits, most were affected in either audition or vision; few children were affected in both modalities. The observed deficits suggest that impaired temporal processing in dyslexia is most evident on tasks that require the ability to synthesize local, temporally modulated inputs into a global percept and the ability to extract the resultant global percept from a noisy environment.
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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 | 0.005 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".