Cracking under pressure: Cognitive load influences performance in youth with NF1
Bibliographic record
Abstract
OBJECTIVES: To extend the current understanding of executive function (EF) deficits in youth with neurofibromatosis type 1 by investigating the impact of cognitive load on performance compared to typically developing children. METHODS: = 42) were drawn from the normative database for the tasks of executive control (TEC). Multivariate and supplementary univariate analyses examined group differences and task effects (inhibitory control and working memory demand). Associations between TEC performance and parent-reported executive dysfunction (BRIEF) were also explored. RESULTS: Both groups showed reduced accuracy and speed with increased inhibitory demand and made fewer errors with increased working memory demand. However, children with NF1 were significantly less accurate and consistent across tasks, particularly under higher cognitive load, while controls improved or maintained performance. Significant group × cognitive load interactions were observed, and laboratory-based deficits in NF1 were associated with parent-reported executive dysfunction. CONCLUSIONS: Children with NF1 experience unique and multidimensional decrements in EF performance in response to increased cognitive load, unlike typically developing peers. These deficits appear to be clinically relevant. Targeting working memory and inhibitory control may reduce susceptibility to cognitive overload and improve outcomes for children with NF1.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".