Exploring motor system contributions to the perception of social information: Evidence from EEG activity in the mu/alpha frequency range
Bibliographic record
Abstract
Putative contributions of a human mirror neuron system (hMNS) to the perception of social information have been assessed by measuring the suppression of EEG oscillations in the mu/alpha (8-12 Hz), beta (15-25 Hz) and low-gamma (25-25 Hz) ranges while participants processed social information revealed by point-light displays of human motion. Identical dynamic displays were presented and participants were instructed to distinguish the intention, the emotion, or the gender of a moving image of a person, while they performed an adapted odd-ball task. Relative to a baseline presenting a nonbiological but meaningful motion display, all three biological motion conditions reduced the EEG amplitude in the mu/alpha and beta ranges, but not in the low-gamma range. Suppression was larger in the intention than in the emotion and gender conditions, with no difference between the latter two. Moreover, the suppression in the intention condition was negatively correlated with an accepted measure of empathy (EQ), revealing that participants high in empathy scores manifested less suppression. For intention and emotion the suppression was larger at occipital than at central sites, suggesting that factors other than motor system were in play while processing social information embedded in the motion of point-light displays.
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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 | 0.001 |
| 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".