Prior Experience but Not Size of Error Improves Motor Learning on the Split-Belt Treadmill in Young Children
Bibliographic record
Abstract
Children can modify learned motor skills, such as walking, to adapt to new environments. Movement errors in these new situations drive the learning. We used split-belt walking to determine whether size of the error affects the degree of learning. Twenty-two children (aged 2-5 y) walked on the split-belt treadmill on two separate days spaced 1 week apart. Twenty-eight adults served as controls. On Day 1, children experienced an abrupt change in belt speeds (from 1:1 to 2:1 differential) resulting in large errors, or a gradual change (same change in speed over 12-15 min), resulting in small errors. Learning was measured by the size of the aftereffect upon return to a 1:1 differential. On Day 2 (1 week later), the leg on the fast belt was reversed, as was the method of introducing the speed differential. We found that the error size did not affect learning. Unexpectedly, learning was greater on Day 2 compared to Day 1, especially for children under 4 y of age, despite the fact that the task was opposite to that of Day 1, and did not influence learning in adults. Hence, 11 additional children under 4 y of age were tested with belts running at the same speed on Day 1, and with a 2:1 speed differential (abrupt introduction) on Day 2. Surprisingly, learning was again greater on Day 2. We conclude that size of error during split-belt walking does not affect learning, but experience on a treadmill does, especially for younger children.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".