Sensory consequences of hand movement following exposure to visual-proprioceptive discrepancy
Bibliographic record
Abstract
In visuomotor adaptation, when subjects first make a reach there is a mismatch between actual and predicted sensory feedback. Subsequently they adapt to accurately reach the target, and update their predictions about sensory feedback which informs changes in hand localization. Additionally, our lab and others have shown that hand proprioception recalilbrates to visual feedback ("proprioceptive recalibration"). Here we quantify the contributions of updated predicted sensory consequences and recalibrated proprioception to hand localization. Ifthere is no discrepancy between predicted and actual visual feedback of the hand during reach training, then any changes in hand localization are due to proprioceptive recalibration. In experiment 1, our subjects trained to actively reach to a target with a 30° rotation. In experiment 2, they were exposed to visual and proprioceptive discrepancy only (cross-sensory error signal; Cressman & Henriques, 2010), but since the apparatus moved the participants' hands there was no prediction about visual consequences to update. Then we measured changes in hand localization, also with both active and passive placement of the adapted hand; i.e. with and without predictions. Hand localization changed substantially in all conditions. With active training, the change for passive placement accounted for two thirds of the change for active placement. As expected, passive training did not lead to a difference between active and passive localization, suggesting both reflect proprioceptive recalibration only. This shows that cross-sensory error signals affect motor performance and lead to proprioceptive recalibration, and thus seem to be an unappreciated aspect of motor learning.
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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 | 0.001 |
| 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".