Effects of task progression characteristics in transfer and dual-task performance
Bibliographic record
Abstract
Studies of implicit motor learning typically use a protocol that consists of progressively more difficult versions of the task during practice (see Poolton & Zachry, 2007 for review). The current experiment compared various types of practice progressions in order to assess their influence on transfer and dual-task . Forty young adults were asked to propel a 3cm disc to a series of targets projected onto a table top. Four experimental groups were included to examine the roles of task difficulty, progression of targets and the position of a transfer and/or dual-task target in relation to the practice targets. We found important effects for task difficulty and the position of a dual-task target in relation to practice targets. Practice to easier (closer) targets produced significantly more error from immediate to delayed transfer, compared to practice with more difficult (farther) targets regardless of the progression followed. This finding suggests an advantage of more difficult practice for transfer to an unpracticed easier target. Practice that started closest to the target used in dual-task tests produced more stable performance on these dual-task tests, compared to those that began with targets farthest from the dual-task test target. These findings provided partial support for the role of implicit learning conditions in practice.Acknowledgments: This study was supported by NSERC
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".