How does visual manipulation affect obstacle avoidance strategies used by athletes?
Bibliographic record
Abstract
Research examining our ability to avoid obstacles in our path has stressed the importance of visual input. The aim of this study was to determine if athletes playing varsity-level field sports, who rely on visual input to guide motor behaviour, are more able to guide their foot over obstacles compared to recreational individuals. While wearing kinematic markers, eight varsity athletes and eight age-matched controls (aged 18-25) walked along a walkway and stepped over stationary obstacles (180° motion arc). Visual input was manipulated using PLATO visual goggles three or two steps pre-obstacle crossing and compared to trials where vision was given throughout. A main effect between groups for peak trail toe elevation was shown with greater values generated by the controls for all crossing conditions during full vision trials only. This may be interpreted as athletes not perceiving this obstacle as an increased threat to their postural stability. Collectively, findings suggest the athletic group is able to transfer their abilities to non-specific conditions during full vision trials; however, varsity-level athletes were equally reliant on visual cues for these visually guided stepping tasks as their performance was similar to the controls when vision is removed.
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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 | 0.002 |
| 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".