How to Make the Word-Length Effect Disappear in Letter-by-Letter Dyslexia
Bibliographic record
Abstract
The diagnosis of letter-by-letter (LBL) dyslexia is based on the observation of a substantial and monotonic increase of word naming latencies as the number of letters in the stimulus increases. This pattern of performance is typically interpreted as indicating that word recognition in LBL dyslexia depends on the sequential identification of individual letters. We show, in 7 LBL patients, that the word-length effect can be eliminated if words of different lengths are matched on the sum of the confusability (visual similarity between a letter and the remainder of the alphabet) of their constituent letters. Additional experiments demonstrate that this result is mediated by parallel letter processing and not by any compensatory serial processing strategy. These findings indicate that parallel processing contributes significantly to explicit word recognition in LBL dyslexia and that a letter-processing impairment is fundamental in causing the disorder.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".