Pattern recall expertise is not affected by moderate physical exercise in female handball players
Bibliographic record
Abstract
General perceptual-cognitive abilities improve under submaximal physical exercise (Pesce, Tessitore, Casella, Pirritano, & Capranica, 2007); however sport-specific skills (e.g. pattern recall) are typically investigated at rest (cf. Williams & Abernethy, 2012). The aim of this study was to examine sport specific pattern recall expertise both at rest and under moderate physical exercise. Thirty-three participants from three groups (handball experts, advanced handball players, and novices) were tested in a handball specific pattern recall task under two conditions, at rest and under a moderate physical exercise of 60 % heart rate reserve (Pesce et al., 2007). We measured pattern recall performance in both conditions as the accuracy of the recalled players’ positions as root-mean square error. An analysis of variance revealed significant main effects for group differences, F(2,30) = 8.49, p < .01, f = .75, with experts performing superior to novices, D = 20.42, p < .01. However, no within subject differences were found between two conditions, F(1, 30) = .48, p = .50, f = .13, 1-s = .22, and the interaction between factors was non-significant, F(2, 30) = .07, p = .94, f = .06, 1-s = .09. Although these results replicate prior research concerning expertise differences in pattern recall tasks the lack of effect for the physical exercise condition might be because the physical exercise was not handball specific (see Manchado et al., 2013) or that submaximal exercise only has a facilitating effect on general perceptual-cognitive abilities which were not influential in this sport-specific pattern recall task.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".