Attention and executive control in varsity athletes engaging in strategic and static sports
Bibliographic record
Abstract
Examining non-sport-related cognitive tasks of attention and executive control in skilled athletes may provide insight into the acquisition of highly specific skills developed in experts as well as help identify successful performance in sport. Through a cross-sectional design, this study examined performance on aspects of attention and executive control among varsity athletes playing soccer (strategic sport) or track & field (static sport) using a computerized test of attention and executive control. Ninety-seven university athletes participating in soccer (n = 50) or track and field (n = 47) were included in the study. Domains of attention and executive control were examined using the Attention Network Test-Interactions (ANT-I). Mean reaction time (RT) and intra-individual variability (IIV) were compared between groups as measures of performance speed and performance stability respectively. Soccer players demonstrated overall faster RTs (p = 0.0499; ηp2 = .04) and higher response accuracy (p = .021, d = .48) on the ANT-I compared to track and field athletes. Faster RTs were observed for soccer players when presented with an alerting tone (p = .029, d = .45), valid orienting cue (p = .019, d = .49) and incongruent flanker (p = .031, d = .45). No significant group differences were observed in IIV (p = .083, d = .36). Athletes engaging in strategic sports (i.e., soccer) demonstrated faster performance under test conditions that required higher vigilance and conflict resolution. These findings suggest that engagement in strategic sports is associated with enhanced performance on non-sport-related cognitive tasks of attention and executive control.
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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 | 0.001 |
| 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) | 0.001 | 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".