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Record W4313325763 · doi:10.54097/ehss.v5i.2927

Autism Spectrum Disorder as a Disorder of Prediction in Sensorimotor Processing

2022· article· en· W4313325763 on OpenAlexaff
Kezhu Niu

Bibliographic record

VenueJournal of Education Humanities and Social Sciences · 2022
Typearticle
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsAutism spectrum disorderPsychologyCognitive psychologyAutismPopulationPrior probabilityEmpirical evidenceDevelopmental psychologyBayesian probabilityArtificial intelligenceComputer science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.363
Threshold uncertainty score0.912

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.051
GPT teacher head0.341
Teacher spread0.290 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2022
Admission routes1
Has abstractyes

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