Bimanual transfer of explicit and implicit contributions to visuomotor adaptation
Bibliographic record
Abstract
Prior research has demonstrated that visuomotor adaptation in one limb, in response to reaching with altered visual feedback of the hand, can be transferred to the untrained limb, specifically when participants are aware of the manipulation (Wang, Joshi, & Lei, 2011). The current study asked if explicit and implicit processes engaged during visuomotor adaptation are transferred from the trained to untrained limb and if these processes are retained. Twelve right-handed participants performed a reach training task to three visual targets while seeing a cursor rotated 40° clockwise relative to their hands on a screen. Participants were instructed on how to counteract the perturbation using a strategy. Following the rotated reach training trials, participants were required to complete two types of no-cursor trials with their trained (left) and untrained (right) hands. Specifically, participants were instructed to aim to the target as accurately as possible (to assess implicit contributions) and to use any strategy they had gained during learning (to assess explicit contributions). Results revealed that explicit and implicit components of visuomotor adaptation transferred to the untrained limb following reach training. While retention of explicit contributions to adaptation was seen in both hands 24 hours after initial training, implicit contributions were not retained in either limb. Together, these results reveal that both implicit and explicit contributions to adaptation can be transferred between limbs and that when participants are provided with a strategy, explicit contributions tend to dominate over time.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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".