Multisensory processing in children with autism: high‐density electrical mapping of auditory–somatosensory integration
Bibliographic record
Abstract
Successful integration of signals from the various sensory systems is crucial for normal sensory-perceptual functioning, allowing for the perception of coherent objects rather than a disconnected cluster of fragmented features. Several prominent theories of autism suggest that automatic integration is impaired in this population, but there have been few empirical tests of this thesis. A standard electrophysiological metric of multisensory integration (MSI) was used to test the integrity of auditory-somatosensory integration in children with autism (N=17, aged 6-16 years), compared to age- and IQ-matched typically developing (TD) children. High-density electrophysiology was recorded while participants were presented with either auditory or somatosensory stimuli alone (unisensory conditions), or as a combined auditory-somatosensory stimulus (multisensory condition), in randomized order. Participants watched a silent movie during testing, ignoring concurrent stimulation. Significant differences between neural responses to the multisensory auditory-somatosensory stimulus and the unisensory stimuli (the sum of the responses to the auditory and somatosensory stimuli when presented alone) served as the dependent measure. The data revealed group differences in the integration of auditory and somatosensory information that appeared at around 175 ms, and were characterized by the presence of MSI for the TD but not the autism spectrum disorder (ASD) children. Overall, MSI was less extensive in the ASD group. These findings are discussed within the framework of current knowledge of MSI in typical development as well as in relation to theories of ASD.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".