Acoustic and Semantic Processing of Auditory Scenes in Children with Autism Spectrum Disorders
Bibliographic record
Abstract
PURPOSE: Processing real-world sounds requires acoustic and higher-order semantic information. We tested the theory that individuals with autism spectrum disorder (ASD) show enhanced processing of acoustic features and impaired processing of semantic information. METHODS: We used a change deafness task that required detection of speech and non-speech auditory objects being replaced and a speech-in-noise task using spoken sentences that must be comprehended in the presence of background speech to examine the extent to which 7-15 year old children with ASD (n = 27) rely on acoustic and semantic information, compared to age-matched (n = 27) and IQ-matched (n = 27) groups of typically developing (TD) children. Within a larger group of 7-15 year old TD children (n = 105) we correlated IQ, ASD symptoms, and the use of acoustic and semantic information. RESULTS: Children with ASD performed worse overall at the change deafness task relative to the age-matched TD controls, but they did not differ from IQ-matched controls. All groups utilized acoustic and semantic information similarly and displayed an attentional bias towards changes that involved the human voice. Similarly, for the speech-in-noise task, age-matched-but not IQ-matched-TD controls performed better overall than the ASD group. However, all groups used semantic context to a similar degree. Among TD children, neither IQ nor the presence of ASD symptoms predict the use of acoustic or semantic information. CONCLUSION: Children with and without ASD used acoustic and semantic information similarly during auditory change deafness and speech-in-noise tasks.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".