Bibliographic record
Abstract
Research participants (Ps) who watch a movie of another person learning to reach in a viscous, rotational force field know better how to compensate for the force field than Ps who did not see the movie. Does information derived from watching another person act directly engage learning mechanisms in the motor system (motor cortex)? If so, motor-learning-by-observing should be degraded by the application of repetitive transcranial magnetic stimulation (rTMS) to the motor cortex after observation. We applied 15 minutes of 1-Hz rTMS to the contralateral motor cortex after subjects watched a movie of another person learning a clockwise (CWFF) or counter-clockwise force field (CCWFF). After observation, all subjects were tested in a clockwise force field (CWFF). Subjects who received rTMS to M1 after watching the CWFF movie performed worse in subsequent CWFF training than controls who did not receive rTMS. In contrast, subjects who received rTMS to M1 after watching the CCWFF movie performed better in subsequent CWFF training than controls who did not receive rTMS. These results replicate earlier findings of motor learning by observing, and in addition show that both the facilitating and interfering effect of observing CWFF and CCWFF learning, respectively, can be attenuated by applying rTMS to M1. Thus, we provide direct evidence that visual information acquired simply by observing another person perform a new motor skill is translated into motor parameters used to update an internal model of that skill.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.008 |
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".