Review of the Evidence on, and Fundamental Questions About, Efforts to Improve Executive Functions, Including Working Memory
Bibliographic record
Abstract
Abstract This systematic review of executive function (EF) interventions is the largest such review thus far, including 179 studies from all over the world, reported in 193 papers. It covers all the ways that have been tried to improve EFs, including computerized and noncomputerized cognitive training, neurofeedback, school programs, physical activities, mindfulness practices, and miscellaneous approaches (e.g., drama and Experience Corps), at all ages. A little studied approach—mindfulness practices involving movement (such as taekwondo and t’ai chi)—shows the best results for improving EFs. Promising school programs are second. Both approaches show better results than any cognitive training. Third best at improving EFs is noncomputerized cognitive training. Perhaps these three approaches show better results than computerized training because they involve more in-person trainer-trainee interaction. The best-performing computerized cognitive-training method for improving EFs is Cogmed®. Support was lacking for claims that N-back training improves fluid intelligence. Resistance training and “plain” aerobic-exercise interventions (e.g., running or walking) show the least evidence of benefit to EFs of all methods. Results for aerobic exercise with more cognitive or motor-skill challenges are only slightly better. This probably reflects how physical-activity interventions have been structured, rather than that physical activity does not benefit EFs. For any intervention, trainers’ ability to make the training activity enjoyable and to communicate their unwavering faith in participants and the program plus the activity being personally meaningful and relevant, inspiring commitment and emotional investment in participants to the activity and to one another is probably what is most important.
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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 | 0.009 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".