Sensory Recalibration of Hand Position Following Visuomotor Adaptation
Bibliographic record
Abstract
Goal-directed reaches are rapidly adapted following exposure to misaligned visual feedback of the hand. It has been suggested that these changes in reaches result in sensory recalibration (i.e., realigning proprioceptive estimates of hand position to match the visual estimates). In the current study we tested whether visuomotor adaptation results in recalibration of hand proprioception by comparing subjects' estimates of the position at which they felt their hand was aligned with a reference marker (visual or proprioceptive) before and after aiming with a misaligned cursor. The misaligned cursor was either translated or rotated to the right of the actual hand location. On the estimation trials, we did not allow subjects to freely move their hands into position. Instead, a robot manipulandum either passively positioned the hand (experiments 1 and 2) or subjects moved their hand along a robot-generated constrained pathway (experiments 3 and 4). We found that regardless of experimental manipulation, subjects' proprioceptive estimates of hand position were more biased to the left after visuomotor adaptation. The leftward shift in subjects' estimates was in the same direction and one third of the magnitude of the adapted movement. This suggests that in addition to recalibrating the sensorimotor transformations underlying reaching movements, visuomotor adaptation results in partial proprioceptive recalibration.
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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 | 0.002 |
| 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) | 0.001 | 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".