Motor-related cortical oscillations distinguish one’s own from a partner’s contributions to a joint action
Bibliographic record
Abstract
The ability to distinguish between one's own and others' actions is a requirement for successful joint action. Such a distinction might be supported by dissociable motor activity underlying each partner's individual contributions to the joint action. However, little research has directly compared motor activity associated with one's own vs. others' actions during joint action. The current study investigated whether motor-related cortical oscillations distinguish between self- and partner-produced actions when partners take turns producing taps to meet a joint timing goal. Across two experiments, the degree of beta suppression differentiated one's own from a partner's actions, with more suppression occurring during one's own actions than during a partner's actions. Self-partner differences in mu suppression were also evident, particularly when partners produced actions in succession. Increased beta suppression was also observed during partners' actions when they were followed by one's own actions, suggesting that the coordination demands imposed by the joint action could affect the pattern of beta reactivity during a turn-taking joint action. Together, these findings demonstrate that dynamic patterns of motor activity underpin successful joint action and that periods of distinct motor activity are associated with one's own contributions to a joint action.
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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 | 0.001 |
| 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".