The effects of specific athletic training with an increase in velocity of locomotion during a collision avoidance task.
Bibliographic record
Abstract
Previous research using an aperture crossing task has shown that specific athletic training influenced actions only when performed in a similar context to that in which it was trained (i.e., increased walking speed) (Higuchi et al., 2011). The objective of the present study is to examine the action strategies of individuals with and without specific athletic training, combined with an increased rate of locomotion during a collision avoidance task. Participants included varsity athletes specifically trained (ST, N=4) at fitting through narrow openings (i.e. offensive positions in football) as well as non-specifically trained individuals (nST, N=3). Participants ran towards a goal placed at the end of a 10m pathway, with two vertically oriented obstacles placed 5m away from the goal on either side of the path’s centre line. The obstacles created an aperture width that ranged between 0.6 and 1.8 (increments of 0.2) times each participant’s shoulder width (SW). Kinematic data was collected using an NDI Optotrak motion tracking system to monitor heading direction and body segment rotation. We hypothesized that athletes specifically trained in a related task will have a smaller Critical Point (i.e., change in action) than the non-specifically trained individuals, when moving at an increased rate of locomotion. Preliminary results revealed that nST individuals were more cautious than the ST individuals (CP=1.6 versus 1.4 SW). Results also revealed that ST individuals passed through obstacles that were between 1.0 and 1.4 SW with postural adjustments (i.e. shoulder rotation or shrug) and only changed travel paths when the apertures were <1.0 SW. Conversely, nST individuals made infrequent postural adjustments and chose to change travel paths at apertures 1.6 SW and smaller. Similar to previous aperture crossing research, ST and nST individuals show differences in action when a task is performed at a similar rate to training. Meeting abstract presented at VSS 2013
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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 | 0.001 |
| 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".