The moderating role of a co-learner when concurrently practicing a balance task
Bibliographic record
Abstract
Research has shown that conditions which promote effort or interference in practice aid long-term retention. Our aim was to test how practicing in a social context could potentially improve the learning of a balance task, by increasing the effort in practice. There is evidence that observing an actor interferes with concurrent action production (Kilner et al., 2003), and the actor's orientation moderates this effect (Sebanz & Shiffrar, 2007). We tested 8 pairs and an alone group (n = 8), across 10, 60-s practice trials on two stability platforms. Pairs practiced front-facing or with one partner back-facing (4 pairs/group). Supporting previous work, observing a partner from the front or back was associated with more imitative (same direction) or compensatory (opposite) movements, respectively. While the front-facing group showed more error (and interference) in practice, all groups improved, and did not differ in retention. However, within the pair groups, those who observed a partner during practice outperformed those who did not on a paired front-facing transfer test. While practice with a partner (and increased interference) did not aid retention, we did see modulations of performance in practice as a function of the partner's orientation. It may be that these types of balance tasks do not benefit from a more effortful mode of practice (promoting a more reactive, than automatic mode of control), or that more sensitive transfer tests are needed to see benefits associated with this more effortful type of practice.Acknowledgments: This research was supported by a Discovery grant from 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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".