Generalization of Motor Learning Based on Multiple Field Exposures and Local Adaptation
Bibliographic record
Abstract
Previous studies have used transfer of learning over workspace locations as a means to determine whether subjects code information about dynamics in extrinsic or intrinsic coordinates. Transfer has been observed when the torque associated with joint displacement is similar between workspace locations-rather than when the mapping between hand displacement and force is preserved-which is consistent with muscle- or joint-based encoding. In the present study, we address the generality of an intrinsic coding of dynamics and examine how generalization occurs when the pattern of torques varies over the workspace. In two initial experiments, we examined transfer of learning when the direction of a force field was fixed relative to an external frame of reference. While there were no beneficial effects of transfer after training at a single location (experiments 1 and 2), excellent performance was observed at the center of the workspace after training at two lateral locations (experiment 2). Experiment 3 and associated simulations assessed the characteristics of this generalization. In these studies, we examined the patterns of transfer observed after adaptation to force fields that were composed of two subfields that acted in opposite directions. The experimental and simulated data are consistent with the idea that information about dynamics is encoded in intrinsic coordinates. The nervous system generalizes dynamics learning by interpolating between sets of control signals, each locally adapted to different patterns of torques.
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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 | 0.001 | 0.006 |
| 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 | 0.001 |
| 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".