Pathogen disgust sensitivity protects against infection in a high pathogen environment
Bibliographic record
Abstract
Disgust is hypothesized to be an evolved emotion that functions to regulate the avoidance of pathogen-related stimuli and behaviors. Individuals with higher pathogen disgust sensitivity (PDS) are predicted to be exposed to and thus infected by fewer pathogens, though no studies have tested this directly. Furthermore, PDS is hypothesized to be locally calibrated to the types of pathogens normally encountered and the fitness-related costs and benefits of infection and avoidance. Market integration (the degree of production for and consumption from market-based economies) influences the relative costs/benefits of pathogen exposure and avoidance through sanitation, hygiene, and lifestyle changes, and is thus predicted to affect PDS. Here, we examine the function of PDS in disease avoidance, its environmental calibration, and its socioecological variation by examining associations among PDS, market-related lifestyle factors, and measures of bacterial, viral, and macroparasitic infection at the individual, household, and community levels. Data were collected among 75 participants (ages 5 to 59 y) from 28 households in three Ecuadorian Shuar communities characterized by subsistence-based lifestyles and high pathogen burden, but experiencing rapid market integration. As predicted, we found strong negative associations between PDS and biomarkers of immune response to viral/bacterial infection, and weaker associations between PDS and measures of macroparasite infection, apparently mediated by market integration-related differences. We provide support for the previously untested hypothesis that PDS is negatively associated with infection, and document variation in PDS indicative of calibration to local socioeconomic conditions. More broadly, findings highlight the importance of evolved psychological mechanisms in human health outcomes.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".