Language and music phrase boundary processing in Autism Spectrum Disorder: An ERP study
Bibliographic record
Abstract
Autism spectrum disorder (ASD) is frequently associated with communicative impairment, regardless of intelligence level or mental age. Impairment of prosodic processing in particular is a common feature of ASD. Despite extensive overlap in neural resources involved in prosody and music processing, music perception seems to be spared in this population. The present study is the first to investigate prosodic phrasing in ASD in both language and music, combining event-related brain potential (ERP) and behavioral methods. We tested phrase boundary processing in language and music in neuro-typical adults and high-functioning individuals with ASD. We targeted an ERP response associated with phrase boundary processing in both language and music - i.e., the Closure Positive Shift (CPS). While a language-CPS was observed in the neuro-typical group, for ASD participants a smaller response failed to reach statistical significance. In music, we found a boundary-onset music-CPS for both groups during pauses between musical phrases. Our results support the view of preserved processing of musical cues in ASD individuals, with a corresponding prosodic impairment. This suggests that, despite the existence of a domain-general processing mechanism (the CPS), key differences in the integration of features of language and music may lead to the prosodic impairment in ASD.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".