The psychophysiology of guilt in healthy adults
Bibliographic record
Abstract
Guilt is a negative emotion, elicited by realizing one has caused actual or perceived harm to another person. Anecdotally, guilt often is described as a visceral and physical experience. However, while the way that the body responds to and contributes to emotions is well known in basic emotions, little is known about the characteristics of guilt as generated by the autonomic nervous system. This study investigated the physiologic signature associated with guilt in adults with no history of psychological or autonomic disorder. Healthy adults completed a novel task, including an initial questionnaire about their habits and attitudes, followed by videos designed to elicit guilt, as well as the comparison emotions of amusement, disgust, sadness, pride, and neutral. During the video task, participants' swallowing rate, electrodermal activity, heart rate, respiration rate, and gastric activity rate were continuously recorded. Guilt was associated with alterations in gastric rhythms, electrodermal activity, and swallowing rate relative to some or all the comparison emotions. These findings suggest that there is a mixed pattern of sympathetic and parasympathetic activation during the experience of guilt. These results highlight potential therapeutic targets for modulation of guilt in neurologic and psychiatric disorders with deficient or elevated levels of guilt, such as frontotemporal dementia, posttraumatic stress disorder, and Obsessive-compulsive disorder.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".