Mind and Body in Sync: The Fascinating Field of Psychophysiology in Sports
Bibliographic record
Abstract
This letter to the chief editor delves into the captivating world of psychophysiology in sports, an interdisciplinary field that explores the dynamic interplay between the mind and body in athletic contexts. It outlines the fundamental concepts of psychophysiology, emphasizing its significance in understanding how psychological states influence physical performance. The letter highlights key historical developments and pivotal research that have shaped our understanding of this field, illustrating how psychophysiological principles have been integrated into sports science. It further discusses the practical applications of these principles in enhancing sports performance, including techniques like biofeedback, mental training, and stress management. The letter addresses existing challenges in the field, such as the need for comprehensive research and the integration of psychophysiological practices in training regimens. It concludes with a forward-looking perspective, emphasizing the potential for future advancements and the importance of continued exploration in this area. The letter aims to draw attention to the importance of psychophysiology in sports, advocating for greater recognition and application within the sports community, and suggesting a pathway for future research and collaboration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.011 | 0.015 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".