Microstate analysis reveals the temporal alignment of mirroring and mentalizing systems
Bibliographic record
Abstract
The aim of the study is to understand how Mirror Neuron System (MNS) and Mentalizing Network (MZN) interact with each other. EEG data was collected during a photo judgment task with pictures of actions or facial expressions. Participants (N = 30, 63% women) were asked to either identify how the shown action/expression was being performed (MNS) or what the goal or intention behind the action was (MZN). Data were analyzed using microstate analysis, source localization and Event-Related Potentials. When comparing the action types, we found early divergence between the brain states of MNS and MZN when comparing the same action type. There was temporal alignment between the start and end time of the induced microstates, among the same action type. Between different action types, the timing was slightly shifted. Temporally, there was a greater overlap between the timing of the states between networks within the same action type as compared to within networks across action types. The MNS and MZN are acting in parallel rather then subsequently and possibly feed into each other. Furthermore, the MNS and MZN do not specifically react to one action type over the other, but their activity is influenced by the action type.
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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) | 0.002 | 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".