Cognitively engaging exercise predicts executive functioning on laboratory tasks
Bibliographic record
Abstract
The cognitive engagement hypothesis claims that regular exercise must be cognitively engaging in order to benefit executive functioning. However, the available evidence for this hypothesis is circumstantial. Here we test it directly in two studies. In Study 1, 145 young adults first reported the extent to which their primary exercise and non-exercise leisure activities were cognitively engaging. They then completed two well-known laboratory tasks measuring executive function: a flanker task to index inhibitory control and a backward digit span task to assess working memory. Structural equation modeling revealed that when participants reported that their exercise relied on inhibitory cognitive control, they performed better on the flanker task, and, when their exercise demanded cognitive flexibility, they performed better on a backward digit task. These relationships did not hold for their primary reported leisure activity. Study 2 confirmed this finding with an independent sample of 227 undergraduates and two different executive function tasks: a stop-signal task to index inhibitory control and a trail making B task to assess cognitive flexibility. When participants reported that their regular exercise relied on inhibitory control they had faster stop-signal reaction times and made fewer trail making errors, and, when their exercise relied on cognitive flexibility, they had slower stop-signal reaction times and longer trail making B completion times. These relationships were again not found for participants’ leisure activities. These findings support the claim that exercise is associated with cognitive performance on laboratory tasks, provided the exercise is itself cognitively demanding.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".