Combining 3D-MOT With Sport Decision-Making for Perceptual-Cognitive Training in Virtual Reality
Bibliographic record
Abstract
This study introduces a virtual life-sized perceptual-cognitive training paradigm that combines three-dimensional multiple object tracking (3D-MOT) with motor (Experiment 1) or perceptual (Experiment 2) sport decision-making tasks. We sought to assess the impact of training on task performance and determine optimal training conditions for improvement and learning. Fifty-seven participants were randomly assigned to one of four training conditions (isolated 3D-MOT, 3D-MOT combined with a decision-making task, consolidated 3D-MOT later combined with a decision-making task, and isolated decision-making task). We evaluated task performance using speed thresholds, success rate (%), and reaction time (s). Findings were that the dual-task paradigm was associated with performance beyond chance level on both 3D-MOT and decision-making tasks despite an important dual-task cost. Interestingly, the results seemed to favor consolidated 3D-MOT training over simultaneous 3D-MOT training when combined with a motor decision-making task but not when combined with a perceptual decision-making task. The number of shared attentional resources in the nature of the additional task (i.e., perceptual or motor decision-making) seems to be key in interpreting the dual-task interference. These findings must be considered when designing representative multitask perceptual-cognitive training.
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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 | 0.002 |
| 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 | 0.001 |
| 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".