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Record W2554839285 · doi:10.1177/1362361316668293

Drawing links between the autism cognitive profile and imagination: Executive function and processing bias in imaginative drawings by children with and without autism

2016· article· en· W2554839285 on OpenAlexaff
Kayla D. Ten Eycke, Ulrich Müller

Bibliographic record

VenueAutism · 2016
Typearticle
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsAutismPsychologyCognitionDevelopmental psychologyTask (project management)PerceptionCognitive psychologyFunction (biology)Executive functionsMental ageRelation (database)Cognitive developmentCognitive style

Abstract

fetched live from OpenAlex

Little is known about the relation between cognitive processes and imagination and whether this relation differs between neurotypically developing children and children with autism. To address this issue, we administered a cognitive task battery and Karmiloff-Smith's drawing task, which requires children to draw imaginative people and houses. For children with autism, executive function significantly predicted imaginative drawing. In neurotypically developing controls, executive function and cognitive-perceptual processing style predicted imaginative drawing, but these associations were moderated by mental age. In younger (neurotypically developing) children, better executive function and a local processing bias were associated with imagination; in older children, only a global bias was associated with imagination. These findings suggest that (a) with development there are changes in the type of cognitive processes involved in imagination and (b) children with autism employ a unique cognitive strategy in imaginative drawing.

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)0.005
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.022
GPT teacher head0.288
Teacher spread0.266 · 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

Citations21
Published2016
Admission routes1
Has abstractyes

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