Awareness governs the extent of visuomotor adaptation
Bibliographic record
Abstract
We asked if awareness of a visuomotor perturbation may govern the extent of visuomotor adaptation achieved. Participants (n = 49) were divided into 2 groups (Abrupt and Gradual group), and reached in a virtual environment where a cursor on a screen misrepresented the position of their hand. In the Abrupt group, the cursor was immediately rotated 45° CW relative to hand motion, whereas in the Gradual group, the 45° cursor perturbation was gradually introduced over trials (1° increments every trial). Participants reached with the rotated cursor (Reach Training) and then with an aligned cursor (Washout). Following Reach Training, participants were designated as aware (Abrupt: n = 15, Gradual: n = 11) and unaware (Gradual: n = 17) based on perceptual reports (e.g., a questionnaire and drawing task where they drew the path their hand took to get the cursor on target). While all participants were able to adapt to the cursor perturbation during Reach Training, unaware participants made faster and more curved movements in order to counteract the cursor rotation compared to aware participants. Performance differences between aware and unaware participants were also seen following removal of the perturbation (Washout). Specifically, analyses revealed that during the early phases of Washout, unaware participants showed greater reach errors than those who were aware, regardless of how the perturbation was introduced during Reach Training. Together, these findings indicate that awareness may govern the extent of (implicit) adaptation to a visuomotor distortion.Acknowledgments: Supported by the Natural Sciences and Engineering Research Council of Canada [EKC].
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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 | 0.005 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".