Autism Spectrum Disorder as a Disorder of Prediction in Sensorimotor Processing
Bibliographic record
Abstract
Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by persistent social interactive and communicative difficulties and repetitive, restricted behavioral patterns. Previous theories suggested impairments in two distinct sets of core abilities as an explanation for ASD. One is the delayed ability to reflect on others’ mental content, and the other is the lack of the tendency to integrate details to create meanings in contexts. In the current field, there is an emergent explanation to consider ASD as a disorder of prediction. Under this notion, two competing views proposed different accounts for the specific deficits in ASD predictive system. The Bayesian view believes that ASD individuals experience reduced priors and are less reliant on top-down information when making predictions. Alternatively, the predictive error view believes that ASD impairments result from a failure to ignore accidental prediction errors caused by environmental noise, leading to overly frequent updates and less generalizable predictions. Though both views seem credible, no previous studies have comprehensively examined their reliability in empirical evidence. Therefore, the present paper fills in the gap by reviewing the two views and their relevant psychological and neuroscientific evidence with a specific focus on sensorimotor prediction. The major conclusion is that most empirical evidence was consistent with the reduced prior proposal but not the prediction error weighing proposal. Specifically, the ASD population is resistant to reliable contextual priors even though their associative learning may remain unimpaired. In keeping with the reduced prior proposal, the ASD population showed atypical connectivity between brain areas, suggesting insufficient communication of top-down information. Additionally, subjective anxiety during the Bayesian inferential process probably hinders the prediction performance. One possible limitation of the present review is the generalizability of conclusions to the domain of social impairments. Future studies should dedicate to exploring the restrictive conditions on the reduced Bayesian prior and E/I ratio imbalance and the role of anxiety in moderating the predictive process. One practical implication is to promote context-dependent imitations in sensorimotor learning in ASD. This review can provide some insights to future intervention studies and practices for children with 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".