Gender comparison of perceptual-cognitive learning in young athletes.
Bibliographic record
Abstract
Elite athletes can demonstrate higher perceptual cognitive abilities compared to non-athletes and those capacities can be trained, leading to higher tracking performances (Faubert, 2013). A recent study showed that differences were observed between male and female athletes in their cognitive abilities whereby male athletes showed superior perceptual abilities compared to female athletes (Legault, Sutterlin-Guindon, Faubert, 2022). The purpose of this study was to investigate whether there were gender differences in athletes’ perceptual cognitive learning using a 3D-MOT tracking task. The study was performed on 72 young people from 17 to 24 years of age; athlete males and females and non-athlete males and females were distributed in four distinct groups. Five separate sessions comprised of three thresholds were performed with each participant. Results indicated that all participants benefited from training and significantly increased their speed thresholds (i.e., participants were able to track targets at a greater speed after 5 sessions). Initial scores showed that male athletes achieved higher speed thresholds than any other groups. Furthermore, after 5 weeks, female athletes obtained higher speed thresholds in comparison to their non-athlete counterparts. In conclusion, engaging in sporting activity is associated with improved perceptual-cognitive abilities and learning. The results support the notion that sport-related activity is beneficial for perceptual-cognitive functions and emphasizes the benefits of participating in sport-related activities for improved brain function with an even greater impact for females.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".