Does "errorless" practice facilitate fine motor-skill learning?
Bibliographic record
Abstract
Implicit motor learning has been studied using an errorless learning protocol that consists of progressively more difficult versions of the task during acquisition (see Poolton & Zachry (2007) for review). Though not truly "errorless" acquisition, the protocol typically results in less error than a reverse order protocol during acquisition, and also better learning as measured in retention and transfer tests. The current experiment was conducted in order to determine if the errorless learning protocol utilized by Maxwell et al. (2001) for a gross motor skill would be replicated in a precise, fine-motor skill. Nineteen young adults were asked to propel a 3cm disc towards a series of targets, varying in size, but constant in location, projected onto a table top following either an errorless or reverse protocol. We found no effects for protocol in acquisition, immediate and delayed retention and transfer tests. Differences between protocol groups were seen only for the largest and smallest of the targets during acquisition. These findings fail to replicate previous effects of an errorless learning protocol, for which we are currently investigating further. Possible reasons for the failure to replicate include amount of practice, nature of the task, and the potential role of task difficulty progressions during 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 | 0.001 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".