Observed effector-independent motor learning by observing
Bibliographic record
Abstract
A compelling idea in cognitive neuroscience links motor control and action observation. Recent work supports the idea that a link exists not just between action observation and action planning, but between observation and motor learning. Several studies support the idea that cortical regions that underlie active motor learning also play a role in motor learning by observing. The goal of the present study was to test whether motor learning by observing is effector dependent (as in active motor learning) or effector independent (as in studies of action observation and mirror neurons). Right-handed human subjects observed a video depicting another individual learning to reach to visual targets in a force field (FF). The video showed reaching in a clockwise FF (CWFF) or a counter-clockwise FF (CCWFF), and depicted an individual reaching with the right or left arm. After observation, all subjects were asked to reach in a CWFF, using their right arm. As in our prior studies, subjects who observed a CWFF prior to the CWFF test performed better than subjects who observed a CCWFF. We show here that this effect was seen both when observers watched others reach using their right arm, and when observers watched others learning to reach using the left arm. These results suggest that information about novel forces learned through observation is represented in an effector-independent coordinate frame, and are consistent with the idea that the brain links not only observation and movement, but motor learning as well, through abstract representations of actions.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".