Developmental Letter Position Dyslexia in Turkish, a Morphologically Rich and Orthographically Transparent Language
Bibliographic record
Abstract
We present the first report of a specific type of developmental dyslexia in Turkish, letter position dyslexia (LPD). LPD affects the encoding of letter positions, leading to letter migrations within words. In a multiple case study of 24 Turkish-speaking children with developmental LPD, we examined in detail the characteristics of this dyslexia and explored its manifestation in Turkish. We used migratable words, in which a migration creates another existing word (e.g., signer-singer), which exposed the migration errors of the participants. In sharp contrast with the common assumption that dyslexics in transparent languages, including Turkish, do not make reading errors, we have shown that right stimuli can detect even up to 30% reading errors. The participants made migrations in reading aloud, comprehension, lexical decision, and same-different tasks, in both words and non-words. This indicates that their deficit is in the orthographic-visual analysis stage, a stage that precedes the orthographic input lexicon and is shared by the lexical and non-lexical routes. Their repetition of non-words and migratable words was normal, indicating that their phonological output stages are intact. They did not make digit migrations in reading numbers, indicating that the orthographic-visual analyzer deficit is orthographic-specific. The properties of Turkish allowed us to examine two issues that bear on the cognitive model of reading: consonant-consonant transpositions were far more frequent than consonant-vowel and vowel-vowel migrations. This indicates that the orthographic-visual analyzer already classifies letters into consonants and vowels, before or together with letter position encoding. Furthermore, Turkish is very rich morphologically, which has allowed us to examine the effect of the morphological structure of the target word on migrations. We found that there was no morphological effect on migrations: morphologically complex words did not yield more (nor fewer) migrations than monomorphemic ones, migrations crossed morpheme boundaries and did not preserve the morphological structure of the target word. This suggests that morphological analysis follows the letter-position encoding stage.
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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 | 0.000 |
| 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 | 0.001 |
| Research integrity | 0.001 | 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".