Decision-making and dynamics of eye movements in volleyball experts
Bibliographic record
Abstract
Key decision-makers among experts in a given field can sometimes be identified based on their role and responsibilities. The aim of the study is to compare perceptual-cognitive skills of experts with decisional responsibilities (setters in volleyball) with that of other volleyball experts. Eighty-two participants (26 setters, 36 other players and 20 controls) viewed 50 volleyball video sequences. Sequences stopped 120 ms before ball contact and participants, whose eye movements were recorded, had to predict the ball direction. Generalized Estimating Equations analysis revealed that setters and controls made more but shorter fixations than other players. However, both expert groups made better predictions than controls. Dynamics analyses of eye movements over time show that, right before ball contact, opposing players' upper body is a most relevant attentional cue in all game situations. Results are discussed in terms of decision-making responsibilities to identify key decision-makers in volleyball and in general. They point towards specific perceptual-cognitive abilities found in setters and support the idea that they constitute a subgroup of experts, but that they are not "better" than other players in anticipating the game.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".