Bibliographic record
Abstract
We describe experiments that test the hypothesis that changes in somatosensory function accompany motor learning. We estimated psychophysical functions relating actual and perceived limb position before, and after two kinds of motor learning: directional motor learning (learning to reach in the presence of novel forces applied by a robot), and non-directional learning (learning to reach quickly and accurately to visual targets, without forces). Following force-field learning, sensed limb position shifted reliably in the direction of the applied force. No sensory change was observed when the robot passively moved the hand through the same trajectories as subjects produced during active learning. Perceptual shifts are reflected in subsequent movements: following learning, movements deviate from their pre-learning paths by an amount similar in magnitude and in the same direction as the perceptual shift. After non–directional motor learning in the absence of forces, we observed improvements in somatosensory acuity following learning. Acuity improvement was seen only in the region of the workspace explored during learning, and not in other locations. No acuity changes were observed when subjects were passively moved through limb trajectories produced during active learning. Taken together, our findings support the idea that sensory changes occur in parallel with changes to motor commands during motor learning, and that the type of sensory change observed depends on the characteristics of the motor task during 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".