Athletes Are Different: Factors That Differentiate Biofeedback/Neurofeedback for Sport Versus Clinical Practice
Bibliographic record
Abstract
Biofeedback and neurofeedback training procedures are often different for athletes than for clinical patients. Athletes come to improve performance whereas patients come to reduce symptoms. This article outlines factors that distinguish work with athletes from work with clinical patients. The differences in training include the purpose of training, the nature of the participant in training, session design, and covert factors underlying the training. Unlike clients, athletes often do intensive transfer of learning training, between 2 and 6 hours of daily sport practice across days, weeks, and months. Although biofeedback and neurofeedback are important factors for enhancing peak performance, there are many covert and overt factors producing performance success such as motivation, intensity of training, “A-ha” experiences, experimental expectancy, behavioral consequences, and mastery learning. The training process with athletes is illustrated through a case example of a young tennis player who mastered control of his anger.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".