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Record W4411330068 · doi:10.1016/j.tics.2025.04.008

Autism-related shifts in the brain’s information processing hierarchy

2025· review· en· W4411330068 on OpenAlexafffund
Boris C. Bernhardt, Sofie L. Valk, Seok‐Jun Hong, Isabelle Soulières, Laurent Mottron

Bibliographic record

VenueTrends in Cognitive Sciences · 2025
Typereview
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsUniversité du Québec à MontréalHôpital Rivière-des-PrairiesMontreal Neurological Institute and Hospital
FundersNatural Sciences and Engineering Research Council of CanadaMinistry of Science and ICT, South KoreaInstitute for Basic ScienceUniversité de MontréalMax-Planck-GesellschaftMcGill UniversityNational Research Foundation of KoreaCanada Research ChairsCanadian Institutes of Health ResearchSick Kids Foundation
KeywordsPsychologyAutismHierarchyCognitive psychologyInformation processingCognitive scienceNeuroscienceDevelopmental psychology

Abstract

fetched live from OpenAlex

Despite considerable research efforts, mechanisms of autism remain incompletely understood. Key challenges in conceptualizing and managing autism include its diverse behavioral and cognitive phenotypes, a lack of reliable biomarkers, and the absence of a framework for integration. This review proposes that alterations in sensory-transmodal brain hierarchy are a system-level mechanism of atypical information processing in autism. Hierarchies can account for diverse autism symptomatology and help explain common neurodevelopmental hallmarks, notably a shift away from socially biased information processing, and an enhanced role, autonomy, and performance of perception. A hierarchical reference frame can also subsume spatially heterogeneous neuroimaging findings and make conceptual contact with foundational theories of cortical information processing, thereby consolidating behavioral, cognitive, computational, and neural characteristics of the condition.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.199
GPT teacher head0.473
Teacher spread0.275 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations22
Published2025
Admission routes2
Has abstractyes

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