Procedural Sequence Learning in Attention Deficit Hyperactivity Disorder: A Meta-Analysis
Bibliographic record
Abstract
Previous literature proposes that the motor deficits in Attention Deficit Hyperactivity Disorder (ADHD) may be attributed to impairments of the procedural memory network, a long-term memory system involved in sensorimotor and cognitive skill development. A handful of studies have explored procedural sequence learning in ADHD, but findings have been inconsistent. A meta-analysis was conducted to begin to establish whether procedural sequence learning deficits exist in ADHD. The results of seven studies comprising 213 participants with ADHD and 257 participants with typical development (TD) generated an average standardized mean difference of 0.02 (CI95 -0.35, 0.39) that was not significant. Heterogeneity was significant across studies and could be partially attributed to the age of participants. We argue that procedural sequence learning appears to be preserved in ADHD and discuss potential explanations for and against this finding.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.022 |
| Bibliometrics | 0.004 | 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) | 0.004 | 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".