Why does intermanual transfer occur?
Bibliographic record
Abstract
After adapting to altered visual feedback of an unseen hand while reaching to visual targets, many studies have shown that the opposite hand also benefits when reaching with the same altered feedback, suggesting intermanual transfer. It is unclear why intermanual transfer occurs. Does transfer occur because the brain is learning new cursor mechanics, which are constant for each hand? If so, then we predict that bimanual transfer should occur when subjects learn to reach with a cursor representing their hand and not an image of their hand. Subjects reached to one of 10 radial targets with an unseen hand. One group of subjects reached with a rotated cursor representing their unseen right hand. Another group of subjects saw a rotated view of their right hand while they performed the same task: these movements were captured using a camera, and displayed in real time on a vertical screen. The motion of the cursor or the image of the hand was rotated either 45° or 105° CCW in the learning condition, where subjects reached for 200 trials with their right hand. Each learning session was followed by a test condition where subjects reached to the same targets under the same viewing condition but with the left, untrained hand for 30 trials. Reaching with the left hand in the cursor condition was significantly less deviated for the first 10 trials of testing compared to the first 10 trials of learning for the 45° rotation (p = .001) and the 105° rotation (p = .001), suggesting intermanual transfer when the cursor was seen. The rotated hand view condition showed no significant transfer for either rotation (p [[gt]] .05). Our results suggest that intermanual transfer may occur because an internal model of the cursor, rather than the arm motor system, is learned.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".