Self-selected conscious strategies do not modulate motor cortical output during action observation
Bibliographic record
Abstract
The human motor system is active not only when actions are performed but also when they are observed. Experimenters often manipulate aspects of the action or context to examine factors that influence this "mirror" response. However, little is known about the role of the observer's own top-down intentions and motivation. In this exploratory study, we investigated whether observers are able to exert conscious control over their mirror response, when they are explicitly instructed to either increase or decrease mirroring. Transcranial magnetic stimulation (TMS) was used to elicit motor-evoked potentials (MEPs) in a thumb abductor muscle as participants (n = 13) watched a video of a hand squeezing a rubber ball. The size of these MEPs, relative to the size of MEPs elicited during fixation cross observation, was taken as an index of mirroring. In an initial block of trials, participants were instructed to merely observe the actions presented. After the first block, the concept of mirroring was explained to the participants, and in the second and third blocks participants were instructed to either increase or decrease their mirror response. We did not instruct them about how to achieve this increase or decrease. Our results showed no difference in either facilitation or absolute motor excitability (i.e., nonnormalized MEP size) between the three blocks, indicating that individuals do not seem to be able to exert control over motor excitability during action observation, at least in the absence of a specific and maintained strategy.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".