Modeling Emotional Arousal With Turbulence Measured by <scp>EEG</scp>
Bibliographic record
Abstract
Turbulence-like dynamics in brain activity have been proposed as a signature of systems operating near criticality, and may reflect changes in neuronal function associated with emotional states. In this paper, we hypothesize that motor behavior linked to emotional expression modulates turbulence, reflecting a shift towards more streamlined brain dynamics characteristic of emotional motor control. We assessed EEG turbulence in 30 healthy participants in a motor paradigm varying in both task demand and degree of emotionality. Conditions included singing, swaying, responding to a virtual conductor of variable expressivity, having your own body movements mirrored by a virtual agent, and combinations thereof. Results showed an inverse relation of turbulence intensity in the alpha range to both degree of movement and perceived level of task emotionality, which was also true for the high gamma range, but to a lesser extent. When factoring in task demand, the effect of level of emotionality in the alpha range deteriorated. This is physiological evidence for why physical arousal is likely to increase the level of perceived emotional engagement or even be misinterpreted as such. Our findings suggest high gamma activity is a more accurate indicator of emotionality during motor tasks and can be key to differentiating EEG signatures of emotional motor control, which has been shown to be partly autonomous from voluntary motor control.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".