No transfer of 3D-Multiple Object Tracking training on game performance in soccer: A follow-up study
Bibliographic record
Abstract
The impact of domain-general cognitive 'brain' training on improving sports performance is highly debated. This study sought to follow-up on research that showcased the benefits of perceptual-cognitive 3D-Multiple Object Tracking (3D-MOT) training in enhancing the on-field performance of soccer players. Additionally, it explored the correlation between athletes' cognitive performance and early career success. Sixty-two males from a professional soccer academy were randomly divided into a dual-task 3D-MOT training group (n = 30) and a control group (n = 32). Participants underwent a 3D-MOT test, a cognitive test of attention, and small-sided games at pre- and post-training. Pre-post-test performances were compared using ANCOVAs. A Chi-squared test evaluated the association between the training regimen and early career success. A Spearman test assessed the correlation between performance on the 3D-MOT, attention test, and early career success. The dual-task 3D-MOT trained group significantly improved its performance on 3D-MOT compared to the control group (p < 0.001). However, no significant pre-post-test differences were observed between the groups in the near-transfer cognitive test and on-field performance (ps > 0.05). There were no associations between the athletes' early career success and the training regimen, and no associations between cognitive test performances and early career success (ps > 0.05). This follow-up study failed to replicate previous findings with dual-task 3D-MOT training unable to produce near or far transfer on soccer performance. In addition, cognitive performance was not related to early career success in this study. The value of cognitive screening and training in sport is discussed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".