A case of developmental deep dyslexia: What's left is right
Bibliographic record
Abstract
Cases of acquired deep dyslexia have not clearly and consistently supported any of the theoretical models. We report on a case of a 51-year-old right-handed female, L.S., with a developmental history of deep dyslexia in order to test the neuropsychological models using a visual half-field semantic priming paradigm. Word targets were primed either by a highly associated word (e.g., CLEAN-DIRTY), a weakly associated word (e.g., CLEAN-TIDY), or an unrelated word (e.g., CLEAN-FAMILY) projected to either the same or opposite visual field (VF) as the target. In normals, RVF-left hemisphere primes result in high associate priming regardless of target location (ipsilateral or contralateral to the prime), whereas LVF-right hemisphere primes produce both high and low associate priming across both target location conditions (Hutchinson, Whitman, Abeare & Raiter, 2003). In contrast, L.S. showed hyperpriming to both high and low associates only in the left hemisphere with inhibition of high associates in the right hemisphere. This case represents a variation of developmental deep dyslexia in which the patient's left hemisphere functions like a normal right hemisphere. However, the lack of exclusively high associate priming in the opposite (right) hemisphere may not provide the necessary narrowing of semantic activation necessary for normal reading and thus, may lead to semantic reading errors. Theoretical implications are discussed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".