Maintenance of perceptual cognitive expertise in female volleyball players
Bibliographic record
Abstract
Understanding how to maintain skill with age is becoming increasingly important and while there is strong evidence that ´hardware´ elements of the perceptual and cognitive system (sensory and cortical factors) decline with age (Andersen, 2012; Brach & Schott, 2003), other components of the visual system and the brain's ability to process (i.e., software elements) visual stimuli change with age (Berke, 2009). Different studies have shown that many elements of experts' superior perceptual and motor performance can be maintained with age (Baker & Schorer, 2009). A recent study by Fischer et al. (2015) focused on the maintenance of perceptual and motor performances in older aged basketball experts, noting maintenance of motor but not perceptual performance (fixation duration) and suggesting older aged experts are able to compensate for losses in perceptual skills. The current study examined aspects of skilled perceptual performance among older female expert (n = 6), advanced (n = 7) and novice (n = 10) volleyball players. As expected, there were skill-related differences among the groups although analyses of differences in perceptual performances between the groups were mixed. Our results highlight several interesting areas for future work including the possibility that age-related changes in the performance environment might drive maintenance or decline of skill. This research contributes to a surprisingly limited evidence base regarding the influence of age on perceptual skill.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".