Interoception in Autism Spectrum Disorder: A review
Bibliographic record
Abstract
PURPOSE: This review article summarizes original scientific research published to date on interoception in individuals with Autism Spectrum Disorder (ASD). Sensory processing has been shown to be atypical in ASD, yet physiological processing and subjective experience of internal sensation processing, namely interoception, has not been reported sufficiently in research or clinical settings. BACKGROUND: There is a small but growing body of scientific research on interoception in ASD, which is relevant to understanding the behavioral and cognitive characteristics inherent in this condition, and may provide a foundation for clinical interventions such as biofeedback, pain management, and brain stimulation techniques. METHODS: A literature review of original research was performed using major scientific databases. RESULTS: Interoception, which occurs due to multisensory connections and integration of internal afferents in cortical and subcortical areas, is atypical in ASD, but the degree and directionality of this abnormality is not yet clear due to the heterogeneity of the condition. Between-group interoceptive differences in individuals with and without ASD have been repeatedly demonstrated, with a slight tendency towards hyporeactivity in interoceptive awareness in individuals with ASD. SIGNIFICANCE: Multidimensional research combining neuroimaging with psychophysiological and self-report measures guided by a clear theoretical model is necessary to understand how interoceptive differences link to the behavioral and cognitive characteristics of ASD. Sensory processing models and autism theory should also be updated to incorporate these recent findings.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".