Severe Multisensory Speech Integration Deficits in High-Functioning School-Aged Children with Autism Spectrum Disorder (ASD) and Their Resolution During Early Adolescence
Bibliographic record
Abstract
Under noisy listening conditions, visualizing a speaker's articulations substantially improves speech intelligibility. This multisensory speech integration ability is crucial to effective communication, and the appropriate development of this capacity greatly impacts a child's ability to successfully navigate educational and social settings. Research shows that multisensory integration abilities continue developing late into childhood. The primary aim here was to track the development of these abilities in children with autism, since multisensory deficits are increasingly recognized as a component of the autism spectrum disorder (ASD) phenotype. The abilities of high-functioning ASD children (n = 84) to integrate seen and heard speech were assessed cross-sectionally, while environmental noise levels were systematically manipulated, comparing them with age-matched neurotypical children (n = 142). Severe integration deficits were uncovered in ASD, which were increasingly pronounced as background noise increased. These deficits were evident in school-aged ASD children (5-12 year olds), but were fully ameliorated in ASD children entering adolescence (13-15 year olds). The severity of multisensory deficits uncovered has important implications for educators and clinicians working in ASD. We consider the observation that the multisensory speech system recovers substantially in adolescence as an indication that it is likely amenable to intervention during earlier childhood, with potentially profound implications for the development of social communication abilities in 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 | 0.001 |
| 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".