Benefits from morphological regularities in dyslexia are task dependent.
Bibliographic record
Abstract
Are difficulties of individuals with dyslexia (IDDs) reduced or enhanced in tasks where linguistic regularities typically facilitate performance, such as vocabulary acquisition and reading? If impaired short-term memory and poor phonological decoding pose the main impediments to IDDs, then they are expected to compensate for these difficulties with a greater reliance on linguistic regularities, to reduce online load. However, if reduced benefits from regularities pose the main bottleneck, IDDs might benefit less than good readers from regularities in spite of their online difficulties. To test that, we administered two experiments. In a novel paradigm of auditory vocabulary acquisition in Hebrew, novel words were presented either with or without familiar morphological structure. Participants with dyslexia showed a reduced recall benefit from familiar structure as compared with controls. However, their recognition was facilitated by morphological structure and did not significantly differ from controls'. In the second experiment, participants read novel words with and without familiar structure. Benefit from structure familiarity for IDDs was significantly smaller than for controls, in spite of IDDs' greater potential benefit from familiar structure due to their reduced overall accuracy. However, when asked to emphasize speed in reading, structure familiarity was found to be beneficial for IDDs, without compromising accuracy. These results imply that accumulative acquisition of sublexical regularities is less efficient in dyslexia, though in some tasks this knowledge is accessible and beneficial. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.004 | 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".