Perceptual-cognitive performance of baseball players with varying level of expertise
Bibliographic record
Abstract
Perceptual-cognitive skills (PCS) are critical to successfully execute motor skills in interceptive sports like baseball. The classification of PCS ranges from purely fundamental (e.g., visual acuity) to high-level and very specific (e.g., quiet eye). Identifying PCS contribution to the execution of baseball skills at the high performance level provides relevant information to support talent selection processes. Evidence supports that expert baseball players present higher PCS performance when compared to novices. However, understanding whether PCS improve concomitantly with the development of expertise will provide valuable information about their interaction in higher skill levels. Thus, this study aimed to compare fundamental PCS performance between players with varying levels of expertise. Thirty-nine male baseball batters aged between 16 and 20 years old (mean = 17.49, SD = 1.07) belonging to two expertise groups (“Élite” and “Relève”) participated in the study. A thorough battery of generic visuo-motor tests (e.g., RightEye Sensorimotor System, Senaptec Sensory Station, Neurotracker, and Vienna Test) was completed by a neuroscientist specialized in sports vision. Analysis of variance of eighteen perceptual-cognitive variables revealed no difference between the expertise levels. Associated with previous studies, this evidence suggests that players require enhanced fundamental PCS to achieve expertise levels in baseball, however, in higher skill levels, PCS do not improve concomitantly with the development of expertise. In sum, this study advances the understanding of the relation between PCS performance and expertise level.
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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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".