Generalization of sensory recalibration in the absence of motor adaptation
Bibliographic record
Abstract
Previous work has demonstrated that motor adaptation occurs following reaches with altered visual feedback of the hand. It has also been shown that the sensory system is recalibrated, such that perception of hand position is shifted following training with a visuomotor distortion (Cressman and Henriques, 2009). In the current study we investigated how these changes in the motor and sensory systems are related by comparing how they generalize across the workspace. Subjects trained to reach to a single target (T1) with distorted hand-cursor feedback, such that they saw a cursor that was rotated 30° clockwise relative to their actual hand position. Following reach training trials, subjects reached to (1) the same target with no visual feedback of their hand; (2) the trained target from a novel start position located approximately 20 cm to the right of the trained start position (T2); and (3) a novel target located approximately 20 cm to the left of the trained target, from the trained start position (T3). Subjects were also required to estimate their felt hand position in two dimensions after moving out from both start positions in order to determine the position at which they perceived their hand was aligned with visual markers located at the same locations as the reach targets. Results indicated that subjects adapted their reaches to a greater extent to the trained target T1 than the novel targets (T2 and T3). In contrast, proprioceptive estimates revealed that subjects recalibrated their estimates of felt hand position to a similar extent at all three targets in the direction of the visual feedback provided. Taken together, the differences in generalization patterns for reach adaptation and proprioceptive recalibration suggest that motor and sensory changes arise independently, potentially due to different error signals present during reaches with altered visual feedback of the hand. Acknowledgments: Supported by NSERC
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.002 | 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".