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Record W2023907251 · doi:10.1167/10.6.13

VEP contrast sensitivity responses reveal reduced functional segregation of mid and high filters of visual channels in Autism

2010· article· en· W2023907251 on OpenAlexafffund
Boutheina Jemel, Daniel Mimeault, Dave Saint‐Amour, A. Hosein, Laurent Mottron

Bibliographic record

VenueJournal of Vision · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRetinal Development and Disorders
Canadian institutionsCentre Hospitalier Universitaire Sainte-JustineUniversité de MontréalHôpital Rivière-des-Prairies
FundersCanadian Institutes of Health Research
KeywordsSpatial frequencyContrast (vision)Autism spectrum disorderAutismVisual processingAudiologyPsychologyNeuroscienceOpticsDevelopmental psychologyPhysicsMedicinePerception

Abstract

fetched live from OpenAlex

Despite the vast amount of behavioral data showing a pronounced tendency in individuals with autism spectrum disorder (ASD) to process fine visual details, much less is known about the neurophysiological characteristics of spatial vision in ASD. Here, we address this issue by assessing the contrast sensitivity response properties of the early visual-evoked potentials (VEPs) to sine-wave gratings of low, medium and high spatial frequencies in adults with ASD and in an age- and IQ-matched control group. Our results show that while VEP contrast responses to low and high spatial frequency gratings did not differ between ASD and controls, early VEPs to mid spatial frequency gratings exhibited similar response characteristics as those to high spatial frequency gratings in ASD. Our findings show evidence for an altered functional segregation of early visual channels, especially those responsible for processing mid- and high-frequency spatial scales.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.007
GPT teacher head0.263
Teacher spread0.255 · 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 designObservational
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

Citations60
Published2010
Admission routes2
Has abstractyes

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