Autonomic Mechanisms of Emotional Reactivity and Regulation
Bibliographic record
Abstract
The ability to perceive and regulate our emotions appropriately is essential for social behavior. Our subjective emotional states to changing external cues are accompanied by physiological changes in heart rate variability (HRV), which is regulated by the sympathetic and parasympathetic branches of the autonomic nervous systems (ANS). In this pilot study, we sought to elucidate the autonomic basis of emotional reactivity and regulation in response to ecologically-valid emotional stimuli—presented in the form of filmclips—in healthy subjects. Subjects watched a series of videos, validated to elicit feelings of amusement, sexual amusement, sadness, fear, and disgust. Subjects were also asked to regulate the outward expression of their response to disgust by suppressing or amplifying it when instructed. Electrodes placed on the torso measured cardiac and respiratory signals, which were processed to compute HRV, which when analyzed with the concurrent respiratory signal calculates measures of parasympathetic activity (RFA, Respiratory Frequency Area, from higher frequencies) and sympathetic activity (LFA, Low Frequency Area, from lower frequencies). Fluctuations in LFA and RFA were computed by the coefficient of variation, and the intensity of the emotional response to the film-clips was captured via questionnaires. Our results suggest that in healthy individuals, higher intensities of subjective emotional experience, both positive (e.g., amusement) and negative (e.g., amplified disgust) elicit higher LFA (sympathetic) responses, whereas emotional regulation is mediated primarily by fluctuations in RFA (parasympathetic) activity. Furthermore, correlations between emotional intensity and components of HRV suggest that higher positive or lower negative emotional states may increase the capacity for emotional regulation via modulation of the parasympathetic component. Our results suggest that a sense of humor might facilitate emotional 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 | 0.000 | 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 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".