Acoustic and neural representation of recognizing different pragmatic intentions from speech prosody in high-functioning ASD children
Bibliographic record
Abstract
Children with autism spectrum disorder (ASD) have difficulties detecting others’ intentions and attitudes from nonverbal cues in social communication. However, their ability to detect pragmatic functions from speech is less understood. This study involved high-functioning ASD children and typical controls listening to speech with different prosodies conveying attitudes towards discussion content (e.g., confident or desired speech) or the listener (e.g., dominant or friendly speech). Participants judged the attitudinal prosodies while their cortico-hemodynamic responses were monitored with functional near-infrared spectroscopy (fNIRS). Analysis showed enhanced cortical activity in the middle/superior temporal gyrus (MTG/STG) and dorsolateral prefrontal areas during content-oriented prosody, and in the dlPFC, inferior frontal gyrus, frontopolar area, and orbitofrontal regions during listener-oriented speech in ASD children compared to the typical development (TD) group. Behaviorally, ASD children showed higher accuracy than TD when hearing listener-oriented speech but similar performance for content-oriented speech. Representational similarity analysis (RSA) indicated that ASD children used both pitch and intensity cues for listener-oriented prosodies but only intensity for content-oriented prosody. This study highlights the altered neurobehavioral profiles of speech perception in children with ASD and underscores the importance of fNIRS in assessing pragmatic functions in high-functioning ASD children.
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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 | 0.001 | 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".