Intermanual transfer and retention of visuomotor adaptation to a large visuomotor distortion are driven by explicit processes
Bibliographic record
Abstract
Reaching with a visuomotor distortion in a virtual environment leads to reach adaptation in the trained hand, and in the untrained hand. In the current study we asked if reach adaptation in the untrained (right) hand is due to transfer of explicit adaptation (EA; strategic changes in reaches) and/or implicit adaptation (IA; unconscious changes in reaches) from the trained (left) hand, and if this transfer changes depending on instructions provided. We further asked if EA and IA are retained in both the trained and untrained hands. Participants (n = 60) were divided into 3 groups (Instructed (provided with instructions on how to counteract the visuomotor distortion), Non-Instructed (no instructions provided), and Control (EA not assessed)). EA and IA were assessed in both the trained and untrained hands immediately following rotated reach training with a 40° visuomotor distortion, and again 24 hours later by having participants reach in the absence of cursor feedback. Participants were to reach (1) so that the cursor landed on the target (EA + IA), and (2) so that their hand landed on the target (IA). Results revealed that, while initial EA observed in the trained hand was greater for the Instructed versus Non-Instructed group, the full extent of EA transferred between hands for both groups and was retained across days. IA observed in the trained hand was greatest in the Non-Instructed group. However, IA did not significantly transfer between hands for any of the three groups. Limited retention of IA was observed in the trained hand. Together, these results suggest that while initial EA and IA in the trained hand are dependent on instructions provided, transfer and retention of visuomotor adaptation to a large visuomotor distortion are driven almost exclusively by EA.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".