Grunting's competitive advantage: Considerations of force and distraction
Bibliographic record
Abstract
BACKGROUND: Grunting is pervasive in many athletic contests, and empirical evidence suggests that it may result in one exerting more physical force. It may also distract one's opponent. That grunts can distract was supported by a study showing that it led to an opponent being slower and more error prone when viewing tennis shots. An alternative explanation was that grunting masks the sound of a ball being hit. The present study provides evidence against this alternative explanation by testing the effect of grunting in a sport-mixed martial arts-where distraction, rather than masking, is the most likely mechanism. METHODOLOGY/PRINCIPAL FINDINGS: We first confirmed that kicking force is increased when a grunt is performed (Experiment 1), and then adapted methodology used in the tennis study to mixed martial arts (Experiment 2). Lifting the foot to kick is a silent act, and therefore there is nothing for a grunt to mask, i.e., its effect on an opponent's response time and/or accuracy can likely be attributed to attentional distraction. Participants viewed videos of a trained mixed martial artist kicking that included, or did not include, a simulated grunt. The task was to determine as quickly as possible whether the kick was traveling upward or downward. Overall, and replicating the tennis finding, the present results indicate that a participant's response to a kick was delayed and more error prone when a simulated grunt was present. CONCLUSIONS/SIGNIFICANCE: The present findings indicate that simulated grunting may distract an opponent, leading to slower and more error prone responses. The implications for martial arts in particular, and the broader question of whether grunting should be perceived as 'cheating' in sports, are examined.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".