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In this study, we examined how beliefs about farm animal sentience and their suffering vary across culture and demographic characteristics. A total of N = 5027) questionnaires were administered in Brazil, Russia, India, China, and the USA. Brazilians showed higher and Chinese lower levels of perceived animal sentience. In Russia and India, the perception of suffering and sentience increases with age, with similar levels to those observed in the USA. In all the countries, more people agreed than disagreed that animals are sentient. Men in India show higher levels of agreement with the relation between eating meat and animal suffering, followed by women in Brazil and China. Lower levels of agreement are observed in Americans and Chinese. Women show higher levels of compassion than men. In Russia, there is a slightly higher level of agreement between men and in the USA younger men agree more. Young American men show higher levels of agreement, while in India and China age has the opposite effect. For fair trading competition, it is important to standardize procedures and respect the demand for both animal protein and its ethical production. Overall, our results showed that perceptions of farm animal sentience and suffering vary substantially across countries and demographic groups. These differences could have important consequences for the perceived ethicality of meat production and consumption, and for global trade in animal products.

The factors influencing human female facial attractiveness—symmetry, averageness, and sexual dimorphism—have been extensively studied. However, recent studies, using improved methodologies, have called into question their evolutionary utility and links with life history. The current studies use a range of approaches to quantify how important these factors actually are in perceiving attractiveness, through the use of novel statistical analyses and by addressing methodological weaknesses in the literature. Study One examines how manipulations of symmetry, averageness, femininity, and masculinity affect attractiveness using a two-alternative forced choice task, finding that increased masculinity and also femininity decrease attractiveness, compared to unmanipulated faces. Symmetry and averageness yielded a small and large effect, respectively. Study Two utilises a naturalistic ratings paradigm, finding similar effects of averageness and masculinity as Study One but no effects of symmetry and femininity on attractiveness. Study Three applies geometric face measurements of the factors and a random forest machine learning algorithm to predict perceived attractiveness, finding that shape averageness, dimorphism, and skin texture symmetry are useful features capable of relatively accurate predictions, while shape symmetry is uninformative. However, the factors do not explain as much variance in attractiveness as the literature suggests. The implications for future research on attractiveness are discussed.