Using video simulations and virtual reality to improve decision-making skills
Bibliographic record
Abstract
A large body of literature supports the effectiveness of video simulation to improve on-court/on-field performance in interceptive tasks (e.g., hitting a baseball). Its effectiveness for invasion tasks requiring the localization of teammates and opponents to select the optimal action has been demonstrated in the laboratory, however transfer of performance gains to the field has yet to be demonstrated. One possibility that could account for the lack of transfer is the relatively modest level of immersion afforded by video simulations using a TV/computer screen, a factor that has been suggested as critical for video training sessions. In this regard, it is noteworthy that modern technology can now afford viewers with an enhanced sense of immersion in the action while using virtual reality. With this in mind, whether presenting video simulations in virtual reality provides an added-value is unknown. Therefore, the present thesis investigates the effect of using video simulations and virtual reality to improve decision-making skills. To do so, 27 varsity-level basketball players underwent four training sessions during which they observed custom-made video clips of basketball plays presented either on a computer screen (CS group), using a virtual reality headset (VR group), or watched footage from NCAA playoff games on a computer screen (CTRL group). Decision-making skills were tested on-court before and after the four training sessions using two types of play: "trained" plays (plays presented during the CS and VR training sessions) and "untrained" plays (plays presented only during the on-court tests). Our results revealed that participant of the VR and CS groups significantly outperformed participants of the CTRL group when facing the “trained” plays during the on-court posttest (mean decision-making accuracy of 79.0%, 73.2% and 57.5%, respectively). However, when facing “untrained” plays, only participants of the VR group demonstrated better decision-making compared to participants of the CTRL group (mean decision-making accuracy of 78.9%, 60.9% and 60.2%, VR, CS, and CTRL groups, respectively). Our results demonstrate that video simulation using a computer screen leads to play specific transfer of performance gains, whereas using a virtual reality headset leads to play specific transfer of performance gains as well as a generalization of learning to novel plays. These results suggest that CS training results in improving pattern recognition of specific plays while VR training results in improving information sampling processes which are generalizable to novel plays.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".