Influence of Gymnastic Background on Triangle Completion Performance in Single and Dual-Task Conditions
Bibliographic record
Abstract
Spatial orientation skills of gymnasts have been investigated in the past, but their navigation skills have not been well described. For instance, little is known on their performance on triangle completion in the absence of vision. The question is whether gymnasts require less attention than non-gymnasts in executing this task. The aims were to study the impact of dual-task on triangle completion performance and reaction time, and to compare this effect in young adults with or without a gymnastic background. Participants were blindfolded and guided along the first two legs of a 5x5 m right angle triangle and then, independently turned and walked towards the origin of this triangle. After they had stopped, their foot position was marked on the floor and angular deviation and linear distance traveled were measured. In the dualtask, reaction time was gathered during the independent walk with participants responding verbally ‘top’ as fast as possible after a sound signal. Gymnasts were found to have smaller angular deviation and longer linear distance traveled than non-gymnasts. Both groups showed longer reaction time in dual-task compared to baseline in sitting and this increase was similar for both groups. The results suggest that gymnastics training improves the perception and control of direction. However, it does not modify perception of linear displacement, nor the attention required to execute the triangle completion task. In dual-task, other cognitive tasks requiring working memory might have had a larger impact on both navigation errors and cognitive task performance.
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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) | 0.001 | 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) | 0.004 | 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".