Bibliographic record
Abstract
Working memory (WM) training explores whether and how repeated practice on working memory tasks might generalize to a variety of outcome measures. Although this field of research is part of the growing literature in cognitive sciences, it has spawned contentious debates. The controversies are largely driven by inconsistent findings and commercial interests, and as a result, numerous meta-analyses and systematic reviews have focused on the validity of WM training. Similarly, there is an inconsistency in the conclusions drawn by these meta-analyses; while there seems to be an agreement about the generalization to proximal cognitive measures; there is a discrepancy in the interpretation of any translational outcomes (e.g., behavioral, clinical, and academic). In this chapter, we review the collection of meta-analyses with a particular focus on children diagnosed with ADHD and other developmental disabilities, and recommend that the field should focus on improving our understanding of the mechanistic and effectiveness properties of WM training, which might result in the development of valuable alternative and/or supplemental approaches, when traditional interventions might fall short, especially for individuals typically underrepresented and underserved.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.049 | 0.017 |
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".