Social neuroscience of disgust
Bibliographic record
Abstract
Disgust can be thought of as an affective system that has evolved to detect signs of pathogens, parasite and toxins as well as to stimulate behaviors that reduce the risk of their acquisition. Disgust incorporates social cognitive mechanisms to regulate exposure to and, or anticipate and avoid exposure to pathogens and toxins. Social cognition entails the acquisition of social information about others (ie, social recognition) and from others (ie, social learning). This involves recognizing and assessing other individuals and the pathogen/parasite/contamination/toxin threat they pose and deciding about when and how to interact with and, or avoid them. Social cognition provides a frame-work for examining the expression of disgust and the associated neurobiological mechanisms. Here, we briefly consider the relations between social cognition and pathogen/parasite/toxin avoidance behaviors. We briefly discuss aspects of: (1) the odor mediated social recognition of actual and potentially infected individuals and the impact of parasite/pathogen threat on disgust mate and social partner choice; (2) the roles of "out-groups" (strangers, unfamiliar individuals) and "in-groups" (familiar individuals) in the expression of disgust and pathogen avoidance behaviors; (3) individual and social learning of disgust and empathy for disgust; (4) toxin elicited disgust and anticipatory disgust; (5) the neurobiological mechanisms, and in particular the roles of the nonapeptide, oxytocin and estrogenic mechanism associated with social cognition and the expression of disgust. These findings on the social neuroscience of disgust have a direct bearing on our understanding of the roles of disgust in shaping human and nonhuman social behavior.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".