Predictive modeling of religiosity, prosociality, and moralizing in 295,000 individuals from European and non-European populations

Pierre O. Jacquet (), Farid Pazhoohi, Charles Findling, Hugo Mell, Coralie Chevallier and Nicolas Baumard ()
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Pierre O. Jacquet: INSERM, PSL Research University
Farid Pazhoohi: University of British Columbia
Charles Findling: INSERM, PSL Research University
Hugo Mell: INSERM, PSL Research University
Coralie Chevallier: INSERM, PSL Research University
Nicolas Baumard: PSL Research University

Palgrave Communications, 2021, vol. 8, issue 1, 1-12

Abstract: Abstract Why do moral religions exist? An influential psychological explanation is that religious beliefs in supernatural punishment is cultural group adaptation enhancing prosocial attitudes and thereby large-scale cooperation. An alternative explanation is that religiosity is an individual strategy that results from high level of mistrust and the need for individuals to control others’ behaviors through moralizing. Existing evidence is mixed but most works are limited by sample size and generalizability issues. The present study overcomes these limitations by applying k-fold cross-validation on multivariate modeling of data from >295,000 individuals in 108 countries of the World Values Surveys and the European Value Study. First, this methodology reveals no evidence that European and non-European religious people invest more in collective actions and are more trustful of unrelated conspecifics. Instead, the individuals’ level of religiosity is found to be weakly but positively associated with social mistrust and negatively associated with the production of behaviors, which benefit unrelated members of the large-scale community. Second, our models show that individual variation in religiosity is well explained by the interaction of increased levels of social mistrust and increased needs to moralize other people’s sexual behaviors. Finally, stratified k-fold cross-validation demonstrates that the structures of these association patterns are robust to sampling variability and reliable enough to generalize to out-of-sample data.

Date: 2021
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DOI: 10.1057/s41599-020-00691-9

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