Bimanual transfer of visuomotor adaptation is driven by explicit adaptation
Bibliographic record
Abstract
Reaching with altered visual feedback of the hand's position in a virtual environment leads to reach adaptation in the trained hand, and also in the untrained hand (Wang & Sainburg, 2002). We asked if reach adaptation in the untrained (right) hand is due to transfer of implicit adaptation (i.e., IA; unconscious) and/or explicit adaptation (i.e., EA; conscious strategy) from the left (trained) hand, and if the transfer of IA and EA change depending on how one is made aware of the visuomotor distortion. Participants (n=60) were divided into 3 groups (Strategy (provided with instructions on how to counteract the visuomotor distortion), No-Strategy (no instructions provided), and Control (EA not assessed)). EA was probed in the Strategy and No-Strategy groups immediately after reaching with a cursor that was rotated 40° clockwise relative to hand motion. IA was assessed at a similar time, for all 3 groups. Results revealed that, while EA was greater for the Strategy versus No-Strategy group, EA transferred between hands for both groups. IA in the trained hand was greatest in the No-Strategy group. IA did not significantly transfer between hands, and in fact, the extent of IA observed in the untrained hand was not related to the extent of IA initially observed in the trained hand. These results suggest that while initial EA and IA in the trained hand is dependent on how one is made aware of the visuomotor distortion, transfer of visuomotor adaptation is driven almost exclusively by EA, regardless of instructions provided.Acknowledgments: Funded by NSERC (Discovery Grant awarded to E. K. Cressman)
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".