Conclusions about interventions, programs, and approaches for improving executive functions that appear justified and those that, despite much hype, do not
Bibliographic record
Abstract
The 'Executive Functions' (EFs) of inhibitory control, working memory, and cognitive flexibility enable us to think before we act, resist temptations or impulsive reactions, stay focused, reason, problem-solve, flexibly adjust to changed demands or priorities, and see things from new and different perspectives. These skills are critical for success in all life's aspects and are sometimes more predictive than even IQ or socioeconomic status. Understandably, there is great interest in improving EFs. It's now clear they can be improved at any age through training and practice, much as physical exercise hones physical fitness. However, despite claims to the contrary, wide transfer does not seem to occur and 'mindless' aerobic exercise does little to improve EFs. Important questions remain: How much can EFs be improved (are benefits only superficial) and how long can benefits be sustained? What are the best methods for improving EFs? What about an approach accounts for its success? Do the answers to these differ by individual characteristics such as age or gender? Since stress, sadness, loneliness, or poor health impair EFs, and the reverse enhances EFs, we predict that besides directly train EFs, the most successful approaches for improving EFs will also address emotional, social, and physical needs.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".