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Record W4412938225 · doi:10.1016/j.reia.2025.202669

Configuration does not affect the allocation of visual attention to foreground and background information differently in autism on a change detection task

2025· article· en· W4412938225 on OpenAlexafffund
Anna-Francesca Boatswain-Jacques, Adina Gazith, France Lainé, Armando Bertone, Jacob A. Burack

Bibliographic record

VenueResearch in Autism · 2025
Typearticle
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsMcGill University
FundersSocial Sciences and Humanities Research CouncilSocial Sciences and Humanities Research Council of CanadaMiriam Foundation
KeywordsAffect (linguistics)Change detectionAutismTask (project management)Cognitive psychologyPsychologyChange blindnessComputer scienceHuman–computer interactionDevelopmental psychologyCommunicationComputer visionEngineering

Abstract

fetched live from OpenAlex

Background The visual processing of autistic people has been connected to a tendency to process local, or detailed, information more readily than the overall global, or configural, structure of visual information. This style has been highlighted in evidence of unique and efficient attention processing on certain tasks, such as the detection of changes. However, research on change detection in autism is mixed, with discrepancies seemingly attributed to the diversity of stimuli and the specific components of perception and attention involved in the task. Purpose We assessed rudimentary differences in change detection between autistic and non-autistic participants using a simple geometric task manipulating local/global processing and foreground/background attention. Method Thirty-two autistic adolescents and adults ( M age = 20.21, SD = 6.10) and 32 mental-age matched non-autistic adolescents and adults ( M age = 19.79, SD = 4.92) completed a change-detection task involving displays of rectangles presented in configural (global) and non-configural (local) arrangements. The participants were asked to indicate whether changes to the colours of these displays occurred. Results Three mixed-effect analyses of variance comparing accuracy, detection sensitivity, and response bias revealed a similar pattern of visual prioritization for both groups, with more accurate, sensitive and less biased change detection for foreground changes, especially non-configural ones. While accuracy levels were similar across groups, non-configural changes produced greater detection sensitivity for non-autistic than autistic participants. Conclusion The findings reflect similar attentional patterns between autistic and non-autistic adolescents and young adults in change detection, even when issues of both configural/non-configural arrangement and foreground/background elements were considered.

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)0.004
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.004
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 science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.084
GPT teacher head0.395
Teacher spread0.310 · 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".

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Citations0
Published2025
Admission routes2
Has abstractyes

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