Can Intentional Distractions Affect Endurance Performance Positively?
Bibliographic record
Abstract
Distractions are often associated with negative outcomes however, distractions can also benefit people. Using the hypothesis of internal-external distractions in the competition for cue, this study examined the effects of an active (controlled) and passive (uncontrolled) distraction on three endurance tasks. Participants (N=42), aged 20 to 23 years were assigned to three groups. Tasks and conditions were counterbalanced across groups to minimize the residual effects of fatigue, learning an intervention and other confounding variables. Performance time, heart rate, ratings of perceived exertion and perceived fatigue were measured. Results showed that active distraction significantly improved performance and lowered Rate of Perceived Exertion in one task. As expected, the active distraction group was the least accurate for estimating time spent. Passive distraction caused minimal performance change. More investigation is needed to understand why an active distraction only affected one trial. Future studies should delve into means for better understanding the hypothesis of competition for cue.
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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 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".