Whose emotion is it? Perspective matters to understand brain-body interactions in emotions
Bibliographic record
Abstract
Feeling happy, or seeing someone feeling happy are two distinct facets of emotions that nevertheless rely on similar physiological and neural activity. The distinction between those two facets is essential, but how is it implemented? In non-emotional cognition, the transient neural response evoked at each heartbeat, or heartbeat evoked response (HER), indexes the self and signals Self/Other distinction. Here, using electroencephalography (n=32), we probe whether HERs’ role in Self/Other distinction extends also to emotion – a domain where brain-body interactions are particularly relevant. We asked participants to rate independently validated affective scenes (n=210), reporting either their own emotion (Self) or the emotion expressed by people in the scene (Other). During the visual cue indicating to adopt the Self or Other perspective, before the affective scene, HERs distinguished between the two conditions, in visual cortices as well as in the right frontal operculum. Physiological reactivity (facial electromyogram, skin conductance, heart rate) during affective scene co-varied as expected with valence and arousal ratings, but also with the Self- or Other- perspective adopted. Finally, HERs contributed to the subjective experience of valence in the Self condition, in addition to and independently from physiological reactivity. We thus show that HERs represent a trans-domain marker of Self/Other distinction, here specifically contributing to experienced valence. We propose that HERs represent a form of evidence related to the ‘I’ part of the judgement ‘To which extent do I feel happy’, which would be combined with the affective evidence proper collected after affective scene onset, accounting at least partly for the difference between feeling an emotion and identifying it in someone else.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".