Early executive dysfunction in ASD: Simple versus complex skills
Bibliographic record
Abstract
Despite the strong evidence of executive function (EF) impairment in Autism Spectrum Disorder (ASD), findings in the preschool period have been inconsistent. Whereas some research has supported an early deficit, many studies have not found early differences in EF. The present study assessed simple and complex components of three EF abilities: working memory, inhibition, and shifting, using a novel battery, the Preschool EF Battery (PEFB; Garon, Smith & Bryson, Child Neuropsychology, 20, 713-736, 2014). Previous research has indicated that the PEFB is sensitive to age differences in typically developing (TD) children aged 18-60 months. Current participants were 34 preschoolers (M = 54.79 months) diagnosed with ASD and 255 TD preschoolers (M = 42.98 months). Results indicated significant differences between the two groups in the three EF abilities, but specific findings were moderated by mental and chronological age. The findings highlight the importance of testing simpler EF skills in very young children with ASD. Autism Res 2018, 11: 318-330. © 2017 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: Given that executive functions (EF) have been found to predict various outcomes such as academic ability and occupational success, sensitive early assessment is important. The current study suggests the presence of early EF deficits in preschool children with autism spectrum disorder. The study highlights the importance of considering how EF develops during early life in typically developing children in order to design sensitive EF measures.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".