The symptoms of surface dyslexia in Arabic: the impact of orthographic ambiguity on reading abilities of a patient with Alzheimer’s disease
Bibliographic record
Abstract
Like other Semitic languages, Arabic is known for its rich morphology and consonantal writing system. In this article, we report the first case of acquired surface dyslexia in an Arabic-speaking patient (HBS). Surface dyslexia is characterised by difficulty reading irregularly spelled words, while performance is better with regular words and nonwords. The purpose of this study was to describe the symptoms of surface dyslexia in Arabic and to investigate how orthographic depth may affect reading in the context of semantic impairment. In HBS, who had Alzheimer's disease, reading was impaired for both words and nonwords. Her reading performance was affected by orthographic ambiguity and by the presence of diacritics depicting short vowels. In particular, she produced mainly vowel errors, suggesting an overreliance on the sublexical route of reading. On the other hand, HBS was able to distinguish long vowels from consonants represented by the same letters, provided there was a real root. This finding can be taken as evidence that HBS could access the word's root to decide whether the vowel letter represents a long vowel or a consonant. The results of this study suggest that the characteristics of surface dyslexia appear to be universal: reading regular words is spared compared to irregular words and non-words. However, the error patterns that HBS showed in reading support a language-specific conceptualisation of the processing components of the lexical and sublexical routes of reading.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".