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Record W1900483600 · doi:10.1002/aur.1359

Age‐Related Changes in Conjunctive Visual Search in Children with and without<scp>ASD</scp>

2014· article· en· W1900483600 on OpenAlexafffund
Grace Iarocci, Kimberly Armstrong

Bibliographic record

VenueAutism Research · 2014
Typearticle
Languageen
FieldNeuroscience
TopicTactile and Sensory Interactions
Canadian institutionsSimon Fraser University
FundersSocial Sciences and Humanities Research Council of CanadaCanadian Institutes of Health ResearchMichael Smith Health Research BC
KeywordsAutismVisual searchPsychologyDevelopmental psychologyAutism spectrum disorderCognitive psychologyCommunication

Abstract

fetched live from OpenAlex

Visual-spatial strengths observed among people with autism spectrum disorder (ASD) may be associated with increased efficiency of selective attention mechanisms such as visual search. In a series of studies, researchers examined the visual search of targets that share features with distractors in a visual array and concluded that people with ASD showed enhanced performance on visual search tasks. However, methodological limitations, the small sample sizes, and the lack of developmental analysis have tempered the interpretations of these results. In this study, we specifically addressed age-related changes in visual search. We examined conjunctive visual search in groups of children with (n = 34) and without ASD (n = 35) at 7-9 years of age when visual search performance is beginning to improve, and later, at 10-12 years, when performance has improved. The results were consistent with previous developmental findings; 10- to 12-year-old children were significantly faster visual searchers than their 7- to 9-year-old counterparts. However, we found no evidence of enhanced search performance among the children with ASD at either the younger or older ages. More research is needed to understand the development of visual search in both children with and without 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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.196
Threshold uncertainty score0.576

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.060
GPT teacher head0.366
Teacher spread0.307 · 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 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

Citations14
Published2014
Admission routes2
Has abstractyes

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