Cognitive Control in Children with ADHD-C: How efficient are they?
Bibliographic record
Abstract
The literature on children with attention deficit/hyperactivity disorder, combined type (ADHD-C), is currently inconclusive as to the nature of deficits in two forms of cognitive control - interference control and response selection (Nigg, 2006). This paper examined the performance of children with ADHD-C on interference control and response selection conflict tasks that required both speed and accuracy. The data was analyzed utilizing a new efficiency method to more effectively analyze overall responses. Both interference control and response selection conditions were combined within tasks allowing for a closer comparison of how children with ADHD-C perform on these specific types of cognitive control. Computerized tasks were administered to 62 boys, ages 7 to 12 (31 controls, 31 ADHD-C). Results revealed deficits in efficient performance for children with ADHD-C on interference control tasks and response selection tasks hypothesized to involve high cognitive control demand. These results highlight the utility of analyzing efficiency data to identify deficits in performance for children with ADHD-C and to foster an increased understanding of cognitive control functioning in this clinical population.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".