A temporal quantitative analysis of visuomotor behavior during four twisting somersaults in elite and sub-elite trampolinists
Bibliographic record
Abstract
Vision has previously been correlated with performance in acrobatic sports, highlighting visuomotor expertise adaptations. However, we still poorly understand the visuomotor strategies athletes use while executing twisting somersaults, even though this knowledge might be helpful for skill development. Thus, the present study sought to identify the differences in gaze behavior between elite and sub-elite trampolinists during the execution of four acrobatics of increasing difficulty. Seventeen inertial measurement units and a wearable eye-tracker were used to record the body and gaze kinematics of 17 trampolinists (8 elites, 9 sub-elites). Six typical metrics were analyzed using a mixed analysis of variance (ANOVA) with the Expertise as inter-subject and the Acrobatics as intra-subject factors. To complement this analysis, advanced temporal eye-tracking metrics are reported, such as the dwell time on areas of interest, the scan path on the trampoline bed, the temporal evolution of the gaze orientation endpoint (SPGO), and the time spent executing specific neck and eye strategies. A significant main effect of Expertise was only evidenced in one of the typical metrics, where elite athletes exhibited a higher number of fixations compared to sub-elites ( p = 0.033). Significant main effects of Acrobatics were observed on all metrics ( p < 0.05), revealing that gaze strategies are task-dependent in trampolining. The recordings of eyes and neck movements performed in this study confirmed the use of “spotting” at the beginning and end of the acrobatics. They also revealed a unique sport-specific visual strategy that we termed as self-motion detection . This strategy consists of not moving the eyes during fast head rotations, a strategy mainly used by trampolinists during the twisting phase. This study proposes a detailed exploration of trampolinists' gaze behavior in highly realistic settings and a temporal description of the visuomotor strategies to enhance understanding of perception-action interactions during the execution of twisting somersaults. • Elite athletes used more tracking fixations than sub-elite athletes. • Trampolinists mainly fixated their gaze on the trampoline bed. • Trampolinists used different visuomotor strategies depending on the acrobatics. • Trampolinists' visual strategies were highly variable between trials and between athletes. • Trampolinists used a uniquely unveiled visual strategy wherein their eyes remained fixed while their body were rotating.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".