Interpreting visual information in motor learning
Bibliographic record
Abstract
Motor learning often requires generalizing previous experience to new situations. One form of generalization is known as bimanual or intermanual transfer, where learning a new task with one hand affects performance of the other arm on the same task. Our study investigates how intermanual transfer is influenced by the visual feedback available when the task is being learned. Previous studies have shown that learning to reach accurately with an imposed visuomotor rotation requires a remapping of the relationship between vision and motor output, and in this study we examined how well this learned remapping transferred between hands under different visual feedback conditions. In our task subjects learned to make accurate reaches to targets with a visuomotor rotation of 45° in two conditions: with normal visual feedback or with visual feedback of their hand reversed so that the subject's right hand looked like their left hand (or vice versa). After a training period with one hand subjects were tested with the opposite hand on the same task to determine how well the learned remapping transferred to the untrained hand. Preliminary findings suggest that learning the remapping with reversed visual feedback results in more transfer of learning to the untrained hand than learning under non-reversed visual feedback conditions. These results suggest that the visual feedback available during motor learning affects generalization to the untrained limb. More specifically, our learning mechanisms adjust motor commands to the limb based not only on proprioception and efference copy but also using visual feedback about the limb.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".