Early‐ and Late‐Stage Auditory Processing of Speech Versus Non‐Speech Sounds in Children With Autism Spectrum Disorder: An ERP and Oscillatory Activity Study
Bibliographic record
Abstract
Individuals with autism spectrum disorder (ASD) often exhibit greater sensitivity to non-speech sounds, reduced sensitivity to speech, and increased variability in cortical activity during auditory speech processing. We assessed differences in cortical responses and variability in early and later processing stages of auditory speech versus non-speech sounds in typically developing (TD) children and children with ASD. Twenty-eight 4- to 9-year-old children (14 ASDs) listened to speech and non-speech sounds during an electroencephalography session. We measured peak amplitudes for early (P2) and later (P3a) stages of auditory processing and inter-trial theta phase coherence as a marker of cortical variability. TD children were more sensitive to speech sounds during early and later processing stages than ASD children, reflected in larger P2 and P3a amplitudes. Individually, twice as many TD children showed reliable differentiation between speech and non-speech sounds compared to children with ASD. Children with ASD showed greater intra-individual variability in theta responses to speech sounds during early and later processing stages. Children with ASD show atypical auditory processing of fundamental speech sounds, perhaps due to reduced and more variable cortical activation. These atypicalities in the consistency of cortical responses to fundamental speech features may impact the development of cortical networks and have downstream effects on more complex forms of language processing.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".