The evolution of disgust for pathogen detection and avoidance
Bibliographic record
Abstract
The behavioral immune system posits that disgust functions to protect animals from pathogen exposure. Therefore, cues of pathogen risk should be a primary driver influencing variation in disgust. Yet, to our knowledge, neither the relationship between current pathogen risk and disgust, nor the correlation between objective and perceived pathogen risk have been addressed using ecologically valid measures in a global sample. The current article reports two studies addressing these gaps. In Study 1, we include a global sample (n = 361) and tested the influence of both perceived pathogen exposure and an objective measure of pathogen risk-local communicable infectious disease mortality rates-on individual differences in pathogen and sexual disgust sensitivities. In Study 2, we first replicate Study 1's analyses in another large sample (n = 821), targeting four countries (US, Italy, Brazil, and India); we then replaced objective and perceived pathogen risk with variables specific to the SARS-CoV-2 pandemic. In Study 1, both local infection mortality rates and perceived infection exposure predicted unique variance in pathogen and sexual disgust. In Study 2, we found that perceived infection exposure positively predicted sexual disgust, as predicted. When substituting perceived and objective SARS-CoV-2 risk in our models, perceived risk of contracting SARS-CoV-2 positively predicted pathogen and sexual disgust, and state case rates negatively predicted pathogen disgust. Further, in both studies, objective measures of risk (i.e., local infection mortality and SARS-CoV-2 rates) positively correlated with subjective measures of risk (i.e., perceived infection exposure and perceived SARS-CoV-2 risk). Ultimately, these results provide two pieces of foundational evidence for the behavioral immune system: 1) perceptions of pathogen risk accurately assay local, objective mortality risk across countries, and 2) both perceived and objective pathogen risk explain variance in disgust levels.
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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 | 0.003 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".