Transferability between virtual and real sports – A training study on dart throwing performance and quiet-eye behaviour in dart novices
Bibliographic record
Abstract
Transfer of training effects between activities (e.g., from basketball to soccer) and training environments (e.g., from practice to competition) is a fundamental issue in motor learning and skill acquisition. Rienhoff et al., (2012) noted the transfer of motor but not quiet-eye effects in basketball experts. The aim of the current study was to investigate whether virtual and real dart training enables the transfer of quiet-eye duration (QED) and throwing accuracy. We hypothesized an increase in performance for both virtual and real dart training and that these skills would transfer between training modalities. Participants ( n = 31) were separated into three groups (virtual training, real training, control) and conducted 15 throws in pre- and post-tests on a real and virtual (Microsoft XBox Kinect) dartboard. The training groups performed three training sessions of 50 throws each. QED was measured using SMI eye tracking glasses and throwing performance (TP) was considered as the radial distance from the bull’s eye. Results showed a significant increase for QED: F (1,26) = 10.63, p = .03, ƒ = .64. The interaction between group and test was significant for TP: Fs(2,28) = 10.39, p = .01, ƒ = .62. All groups improved their QED between pre and post-tests with the virtual group showing the highest increase. Concerning TP, the training groups maintained their results between tests with the control group performing worst. Our results indicate that virtual training facilitates performance and transferability of QED. This might be because of a cross-hair presented in the video game. However, our results suggest real training is needed to increase TP and its transferability. An explanation might be a longer needed intervention for improving both factors and in addition that both tasks are not as simultaneously as supposed to be.
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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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".