Aberrant Neural Entrainment to Word-Level Speech Patterns in Fragile X Syndrome: Evidence for a Statistical Learning Deficit
Bibliographic record
Abstract
Fragile X syndrome (FXS), the most common inherited cause of intellectual disability and autism spectrum disorder, causes significant language and cognitive impairments. Statistical learning refers to the ability to extract patterns from sensory input through mere exposure and plays a central role in language acquisition. Surprisingly, statistical learning in FXS has not been explored. Given that children with FXS typically follow a delayed developmental trajectory for language, we hypothesized that they would show impaired statistical learning. To test this hypothesis, we used an EEG measure of neural entrainment to index statistical learning of hidden trisyllabic words within a continuous speech stream in children with FXS (n = 17) and in typically developing controls (n = 31). Children with FXS showed significantly reduced neural entrainment to words compared to controls, particularly in the superior temporal gyrus and transverse temporal gyrus (primary auditory cortex), providing evidence of statistical learning impairment. Notably, syllable-level entrainment was preserved or even enhanced in FXS, indicating that word-level deficits cannot be attributed to general auditory processing impairments. In addition, while typically developing controls showed an increase in word-level entrainment over the course of learning, children with FXS failed to show a similar increase over time. Taken together, this pattern of results demonstrates that children with FXS can process rapid, lower-order acoustic structure but struggle to integrate these syllables into longer, chunk-like word representations. Overall, these findings suggest that statistical learning is impaired in FXS and also suggest neural entrainment to statistical structure as a potential therapeutic target.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".