Lexical morphology as a source of risk and resilience for learning to read with dyslexia: An fNIRS investigation
Bibliographic record
Abstract
Purpose: To understand the role of meaning-based skills in learning to read with dyslexia, we examined the neuro-cognitive bases of lexical morphology in children of varied reading abilities.Method: Children completed auditory morphological and phonological awareness tasks during functional near-infrared spectroscopy neuroimaging. We first examined the relation between lexical morphology and phonological processes in typically developing readers (Study 1, N = 66, Mage = 8.39), followed by a more focal inquiry into lexical morphology processes in dyslexia (Study 2, N = 50, Mage = 8.62). We then conducted a data-driven network analysis to examine functional connectivity during lexical morphology processes in all participants (Study 3, N = 91, Mage = 8.77).Results: Typical readers exhibited stronger engagement of language neurocircuitry during the morphology task relative to the phonology task, suggesting that morphological analyses involve a synthesis of multiple components of sublexical processing. This effect was stronger for more analytically complex derivational morphemes (like+ly) relative to more semantically transparent free root morphemes (snow+man). In contrast, children with dyslexia exhibited stronger activation during the free root relative to derivational morpheme conditions, possibly because children with dyslexia use semantic information to boost word recognition. Data-driven and person-specific functional connectivity analyses revealed two groups of readers with either denser fronto-temporal or temporal-only connections. Stronger readers with and without dyslexia were more likely to fall into the fronto-temporal group.Conclusions: This study informs literacy theories by identifying an interaction between reading ability, word structure, and the way that the developing brain learns to recognize words in speech and in print.
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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) | 0.000 | 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 | 0.000 | 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".