Amount of choice in discovery motor learning: A performance-learning paradox
Bibliographic record
Abstract
Discovering how to move accurately and consistently is a fundamental feature of motor learning. Our goal in the present study was to evaluate the complementary roles of amount of choice vs guidance in a serial, discovery motor-learning task. Participants in each of four separate groups practiced four, 7-sequence keypress patterns in which each correct arrow keypress moved a colour-coded cursor through a computer monitor grid. For each sequence step in the "1-choice" group, the computer illustrated which arrow key to press next. In the "4-choice" group, no sequence steps were illustrated, thus forcing participants to discover each step without guidance. The remaining groups had either two or three key choices on each sequence step. The results revealed that amount of choice had opposite effects on acquisition performance and learning. The 4-choice group produced more errors and longer MTs than the 1-choice group during practice, but fewer errors and shorter MTs in retention. The 2- and 3-choice groups performed with intermediate amounts of error and MT in both acquisition and retention. All groups performed well in the 1-choice transfer test; however, the 1-choice group experienced considerable difficulty in the 4-choice transfer test. Contrary to expectations, the 4-choice group expressed greater learning confidence than the other groups. Together, these findings suggest that increased amounts of choice degrade motor performance, but enhance learning and metamemory processes.Acknowledgments: This study was funded 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 | 0.004 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".