Exploring the effect of simulated crowd noise on multiple object tracking performance in usport football athletes
Bibliographic record
Abstract
Background noise is present in most daily activities. For some individuals, this noise can be dismissed; for others however, background noise can have a negative effect on an individual's ability to perform (e.g., SA¶derlund et al., 2010). Little work has been conducted on how attentionally-based performance (i.e., multiple object tracking, MOT) is affected when audio stimuli is present in athletic populations. The objective of this study was to examine if and how noise impacted MOT performance in university level football players. Twenty USPORT level football athletes (M age = 20.45 yrs, SD = 1.65 yrs) participated in a 6-session (18 trials) 3D-MOT training experience using the NeurotrackerTM (Cognisens Inc.). Ten athletes completed the training in a dark room with no external noise (had noise cancelling headphones); while the other ten athletes completed the training in the same room but were exposed to a consistent simulated crowd noise. No significant differences in baseline visual tracking speed (VTS) scores between the two groups (p > 0.05) were found. After the 18 training sessions, the mean VTS score for the noise group was 2.07, SD = 0.24. The no noise group averaged significantly slower, t(1, 18) = 2.4, p < 0.028 at M = 1.77, SD = 0.32. Athletes typically perform in loud stadiums and these findings could be explained by the ability to block out external distractions. Indeed, the presence of the simulated crowd noise may actually enhance the ecological validity of the training sessions. Limitations and future directions will be discussed.Acknowledgments: Like to acknowledge the private donation to the University of Regina's Sport Psychology Laboratory
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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 | 0.001 |
| 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".