Effects of Separate Cognitive Training on Endurance Exercise Performance
Bibliographic record
Abstract
Background: Combined cognitive and physical training develops resilience to mental fatigue, reduces perceived effort, and improves endurance exercise performance when compared to physical training and no training. The isolated contribution of cognitive training toward endurance performance has yet to be determined. Accordingly, we examined the effects of separate cognitive training on endurance exercise performance. Method: Two studies employed a pre-test/training/post-test design, with participants randomly assigned to cognitive training or control groups. At pre-test and post-test, participants completed a rhythmic handgrip task (Study 1) or a graded exercise test on a cycle ergometer (Study 2). In Study 1, the cognitive training group completed 20 sessions (four 20 min sessions per week for five weeks) of cognitive training (incongruent Stroop and 2-back tasks), whereas the control group completed no training. In Study 2, the cognitive training group completed nine sessions (three 10 min sessions per week for three weeks) of cognitive training (incongruent Stroop, stop-signal and typing inhibition tasks), whereas the control group completed nine sessions of sham training (congruent Stroop, sham stop-signal and sham typing inhibition tasks). Endurance exercise performance was measured as force production (Study 1) and time to exhaustion (Study 2). Heart rate, exertion and fatigue were also measured. Results: Endurance performance, indexed by force production (Study 1) and time to exhaustion (Study 2), did not change from pre-test to post-test and did not differ between cognitive training and control groups. Similarly, ratings of perceived exertion and heart rate during the exercise tasks did not differ between cognitive training and control groups (Studies 1 and 2). Conclusions: Since separate cognitive training did not improve exercise endurance performance, combined training should be used to create a synergistic training stimulus for brain adaptation and performance enhancement.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".