Targeting the mind and body: recommendations for future research to improve children’s executive functions
Bibliographic record
Abstract
Children’s executive functions (EFs)--the cognitive processing underlying controlled, goal-oriented cognition and behavior--have been shown to be important predictors of future physical, mental, and social wellbeing. Thus, developmental researchers are keen to uncover effective methods to improve children’s EFs. While much of the focus in the past decade has been on direct cognitive and behavioral interventions to improve children’s EFs, another line of research--typically undertaken in medical schools and in departments of kinesiology--has examined physical health interventions as a way to indirectly improve children’s EFs. This commentary suggests that there is promising evidence that physical activity-based interventions to increase children’s fitness also enhance children’s EFs. There is ample need for additional studies to firmly establish this effect, and to determine the degree to which intervention effects transfer from laboratory EF assessments to ‘real-world’ functioning. Finally, there is intriguing evidence from animal models that interventions that combine physical and cognitive training have robust positive impacts on brain health. To translate these findings to humans, there is a need for collaborations between developmental psychologists and physical health experts in order to design interventions that simultaneously target children’s physical and cognitive health.
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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 | 0.022 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.015 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.019 | 0.020 |
| Insufficient payload (model declined to judge) | 0.030 | 0.008 |
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".