A psychophysiological approach to the study of strategic self-talk mechanisms through heart rate variability during a cycling task
Bibliographic record
Abstract
Introduction: The self-talk literature has stressed the importance of employing multidisciplinary approaches to better understand how strategic self-talk operates. Objective: The purpose of the present study was to examine the effect of a strategic self-talk intervention on vagal modulation during moderate-intensity aerobic exercise. Methodology: A randomized control trial was employed. Eighty-three sport science students (36 females) with a mean age of 21.02 (±2.31) years, completed the requirements of the study. Participants were randomly assigned to experimental and control groups. Heart rate variability was monitored during exercise to assess changes in vagal modulation. Participants cycled at 50% of their heart rate reserve for 20 minutes, with the experimental group using strategic instructional self-talk. The heart rate variability measures were averaged to 4-minute time intervals and analyzed using repeated measures analysis of variance. Results: Analysis of pairwise comparisons showed significant differences between the two groups for RMSSD during the final 8-minute of the task (p< .05). Discussion: These differences reflected less vagal suppression for the self-talk group compared to the control group. The observed differences in vagal modulation may be explained by the self-regulating effects of strategic self-talk during the task, particularly in the later parts, suggesting a less effortful performance. Conclusions: The present findings provide encouraging evidence to further investigate the strategic self-talk mechanisms through psychophysiological approaches.
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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 | 0.001 |
| 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 | 0.000 | 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".