Effects of Reflection to Improve Goal-Directed Self-Talk on Endurance Performance
Bibliographic record
Abstract
We investigated the effects of an intervention that encouraged reflection on organic self-talk used during endurance performance. Using an experimental design, we compared the effects of enhancing metacognitive skills by (a) planning and (b) reviewing and evaluating goal-directed self-talk. Participants completed three time-to-exhaustion cycling task trials in which we hypothesized that the intervention group would perform significantly better than the control group. Further, we expected a reduction in perceived exertion for a given workload among participants following a self-talk intervention. Thirty-four participants completed a time-to-exhaustion cycle ergometer test, after which participants were randomly divided into an intervention and control group. The intervention group performed reflection tasks on performance in the time-to-exhaustion test. Participants completed two further time-to-exhaustion tests. Repeated measures analyses of covariance to test whether the intervention group performed for longer indicated no significant difference in time to exhaustion (p = 0.157). Perceived exertion rates were 2.42% higher in the intervention compared to the control group (p = 0.025). In conclusion, in the intervention group, goal-directed self-talk led to increased sensitisation to perceived exertion, and participants chose to stop exercising at this point rather than repeat implementation of self-talk statements and persist for longer.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".