Atypical speech versus non-speech detection and discrimination in 4- to 6- yr old children with autism spectrum disorder: An ERP study
Bibliographic record
Abstract
Previous event-related potential (ERP) research utilizing oddball stimulus paradigms suggests diminished processing of speech versus non-speech sounds in children with an Autism Spectrum Disorder (ASD). However, brain mechanisms underlying these speech processing abnormalities, and to what extent they are related to poor language abilities in this population remain unknown. In the current study, we utilized a novel paired repetition paradigm in order to investigate ERP responses associated with the detection and discrimination of speech and non-speech sounds in 4- to 6-year old children with ASD, compared with gender and verbal age matched controls. ERPs were recorded while children passively listened to pairs of stimuli that were either both speech sounds, both non-speech sounds, speech followed by non-speech, or non-speech followed by speech. Control participants exhibited N330 match/mismatch responses measured from temporal electrodes, reflecting speech versus non-speech detection, bilaterally, whereas children with ASD exhibited this effect only over temporal electrodes in the left hemisphere. Furthermore, while the control groups exhibited match/mismatch effects at approximately 600 ms (central N600, temporal P600) when a non-speech sound was followed by a speech sound, these effects were absent in the ASD group. These findings suggest that children with ASD fail to activate right hemisphere mechanisms, likely associated with social or emotional aspects of speech detection, when distinguishing non-speech from speech stimuli. Together, these results demonstrate the presence of atypical speech versus non-speech processing in children with ASD when compared with typically developing children matched on verbal age.
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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) | 0.001 | 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".