Embedding Perceptual–Cognitive Training in the Athlete Environment: An Interdisciplinary Case Study Among Elite Female Goalkeepers Preparing for Tokyo 2020
Bibliographic record
Abstract
Goalkeepers play a very specific and crucial role in water polo. They rely on advanced perceptual–cognitive (P–C) skills to make fast and accurate decisions. However, their daily training environment often lacks stimulation and representativeness of game demands. This was exacerbated by the COVID-19 pandemic, where noncontact practices became the “new normal.” In the Canadian Women’s National Team preparation for the 2020 Olympics, goalkeepers’ P–C training was made a priority. Led by the team’s mental performance consultant and experts from transdisciplinary fields, the initiative began with an evaluation of a broad range of P–C skills in goalkeepers. Leading up to the Olympics, a series of P–C activities (i.e., anticipatory training using video occlusion, eye–hand coordination, and visuomotor drills) were adopted based on ecological dynamics principles. Virtual reality technology and constraints-focused tools were used to enrich and diversify the goalkeepers’ daily trainings environment. This case study reports the evaluation of P–C skills, the context, and the way in which the P–C activities were implemented, as well as their holistic impact on goalkeepers. Reflections and limitations are also shared to encourage interdisciplinary efforts in sport psychology and increase awareness among mental performance consultants about the importance of psycho-perceptual-motor skill training for mental performance.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".