Executive Functions in Children With Specific Language Impairment: A Meta-Analysis
Bibliographic record
Abstract
Purpose: Mounting evidence demonstrates deficits in children with specific language impairment (SLI) beyond the linguistic domain. Using meta-analysis, this study examined differences in children with and without SLI on tasks measuring inhibition and cognitive flexibility. Method: Databases were searched for articles comparing children (4-14 years) with and without SLI on behavioral measures of inhibition or cognitive flexibility. Weighted average effect size was calculated using multilevel modeling to measure potential group differences. Results: The analysis included 46 studies. Of those, 34 included inhibitory control measures and 22 included cognitive flexibility tasks. Children with SLI performed below same-aged peers on both inhibitory control tasks (g = -.56) and cognitive flexibility tasks (g = -.27). Moderator analyses showed no effect of linguistic task demands, participant age, or severity of language impairment on the degree of difference between children with SLI and controls on measures of inhibitory control. Conclusion: Reliable differences between children with and without SLI were found on inhibition and cognitive flexibility tasks. A moderate group effect was found for inhibition tasks, but there was only a small effect for cognitive flexibility tasks. Results of moderator analyses suggest that these deficits are present throughout development despite task demands or severity of linguistic impairment.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.019 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".