The Effect of Constant or Variable Training Distance on the Generalization of Throwing Skills
Bibliographic record
Abstract
Background: Generalization is a vital aspect of real-life motor learning. We asked whether in a realistic skill (bean bag throwing) generalization occurs within or beyond the range of trained movements and whether this is different for constant or variable practice. Methods: what was your outcomes? How you measured them? In two experiments participants threw beanbags at a target at various distances. In the first experiment (n=24), two training groups threw beanbags to a constant near or far target and were examined at an intermediate transfer test. In the second experiment (n=80), participants trained either at a single target (constant), or two targets alternatingly (variable) with targets placed at different distances and they were tested for transfer within and beyond the training range. A control group was included which only performed the transfer tasks. Results: For the near transfer target, no group outperformed controls (P>.05), whereas all groups except the near constant group (P=.072) performed better than the control group at the intermediate target, and only the far constant training group performed better than controls at the far target (P<.02).
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".