Reach Adaptation and Proprioceptive Recalibration Following Exposure to Misaligned Sensory Input
Bibliographic record
Abstract
Motor adaptation in response to a visuomotor distortion arises when the usual motor command no longer results in the predicted sensory output. In this study, we examined if exposure to a sensory discrepancy was sufficient on its own to produce changes in reaches and recalibrate the sense of felt hand position in the absence of any voluntary movements. Subjects pushed their hand out along a robot-generated fixed linear path (active exposure group) or were passively moved along the same path (passive exposure group). This fixed path was gradually rotated counterclockwise around the home position with respect to the path of the cursor. On all trials, subjects saw the cursor head directly to the remembered target position while their hand moved outwards. We found that after exposure to the visually distorted hand motion, subjects in both groups adapted their reaches such that they aimed ∼6° to the left of the intended target. The magnitude of reach adaptation was similar to the extent that subjects recalibrated their sense of felt hand position. Specifically the position at which subjects perceived their unseen hand to be aligned with a reference marker was the same as that to which they reached when allowed to move freely. Given the similarity in magnitude of these adaptive responses we propose that reach adaptation arose due to changes in subjects' sense of felt hand position. Moreover, results indicate that motor adaptation can arise following exposure to a sensory mismatch in the absence of movement related error signals.
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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 | 0.001 |
| 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) | 0.002 | 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".