The effects of sport-specific training on perceptions and actions during a gap crossing task
Bibliographic record
Abstract
Athletes have excellent knowledge of their action capabilities. They can therefore be studied to understand elite action strategies through perception-action integration 1 . It is hypothesized that specifically trained field athletes (rugby, soccer, lacrosse) will have more accurate perceptions of their body size (shoulder width, SW) when avoiding obstacles. These athletes train under time constraints, and are therefore believed to be more accurate when avoiding obstacles in near space, compared to untrained individuals. The purpose of this study was to determine how athletic training influences perception-action integration when navigating around or through spaces. Specifically-trained athletes (N=12) and non-athlete controls (N=8) performed perceptual judgements of passability through varying gap sizes. Participants were asked to walk toward the midline of a body-scaled gap (0.9-1.7 times participants’ SW in 0.2 increments) created by two obstacles, located 5m from the start location and were asked to judge whether they could safely pass between the obstacles, without changing their body dimensions (shrugging, or rotating shoulders). The “yes” or “no” response was recorded. In the obstacle avoidance task, participants walked along a 10m long pathway toward a goal. Two obstacles were again used to create a body-scaled gap ranging between 0.9 and 1.7xSW (0.2 increments) and placed 3m, 5m, or 7m from the start location. Participants were not instructed on how to avoid the obstacles, but were told not to hit them. A repeated measures ANOVA revealed specifically-trained athletes perceived safe passage through gaps greater than or equal to 1.1xSW, where non-athletes perceived safe passage through gaps greater or equal to 1.3xSW (F (1) =3.97, p<.05). However, both groups tended to walk through gaps greater or equal to 1.3xSW, regardless of obstacle distance (F (4) =74.89, p<.05). These results suggest that non-trained individuals may have more consistent perceptual judgements and action strategies than specifically-trained athletes. 1. Vickers (2007). Human Kinetics, Champaign, IL.
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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 | 0.000 | 0.003 |
| 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 | 0.001 |
| 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".