Alexia With and Without Agraphia: An Assessment of Two Classical Syndromes
Bibliographic record
Abstract
BACKGROUND: Current cognitive models propose that multiple processes are involved in reading and writing. OBJECTIVE: Our goal was to use linguistic analyses to clarify the cognitive dysfunction behind two classic alexic syndromes. METHODS: We report four experiments on two patients, one with alexia without agraphia following occipitotemporal lesions, and one with alexia with agraphia from a left angular gyral lesion. RESULTS: The patient with occipital lesions had trouble discriminating real letters from foils and his reading varied with word-length but not with linguistic variables such as part of speech, word frequency or imageability. He read pseudo-words and words with regular spelling better, indicating preserved use of grapheme-to-phoneme pronunciation rules. His writing showed errors that reflected reliance on 'phoneme-to-grapheme' spelling rules. In contrast, the patient with a left angular gyral lesion showed better recognition of letters, words and their meanings. His reading was better for words with high imageability but displayed semantic errors and an inability to use 'grapheme-to-phoneme' rules, features consistent with deep dyslexia. His agraphia showed impaired access to both an internal lexicon and 'phoneme-to-grapheme' rules. CONCLUSION: Some cases of pure alexia may be a perceptual word-form agnosia, with loss of internal representations of letters and words, while the angular gyral syndrome of alexia with agraphia is a linguistic deep dyslexia. The presence or absence of agraphia does not always distinguish between the two; rather, writing can mirror the reading deficits, being more obvious and profound in the case of an angular gyral syndrome.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".