The Computations Underlying Religious Conversion: A Bayesian Decision Model

Inspired by recent Bayesian interpretations about the psychology underlying religion, the paper introduces a theory proposing that religious conversion is shaped by three factors: (i) novel relevant information, experienced in perceptual or in social form (e.g., following interaction with missionari...

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Κύριος συγγραφέας: Rigoli, Francesco (Συγγραφέας)
Τύπος μέσου: Ηλεκτρονική πηγή Άρθρο
Γλώσσα:Αγγλικά
Έλεγχος διαθεσιμότητας: HBZ Gateway
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Interlibrary Loan:Interlibrary Loan for the Fachinformationsdienste (Specialized Information Services in Germany)
Έκδοση: 2023
Στο/Στη: Journal of cognition and culture
Έτος: 2023, Τόμος: 23, Τεύχος: 1/2, Σελίδες: 241-257
Άλλες λέξεις-κλειδιά:B Utility
B Mysticism
B religious reasoning
B Bayesian
B Religious Conversion
B Syncretism
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Περιγραφή
Σύνοψη:Inspired by recent Bayesian interpretations about the psychology underlying religion, the paper introduces a theory proposing that religious conversion is shaped by three factors: (i) novel relevant information, experienced in perceptual or in social form (e.g., following interaction with missionaries); (ii) changes in the utility (e.g., expressed in an opportunity to raise in social rank) associated with accepting a new religious creed; and (iii) prior beliefs, favouring religious faiths that, although new, still remain consistent with entrenched cultural views (resulting in the phenomenon of syncretism). From the theory, a multifactorial picture of conversion emerges. Based on which factor is primarily engaged in each case, a classification of different types of conversion can be derived, with a remarkable fit with empirical literature. The theory offers a description of the processes underlying religious conversion and, highlighting the links among apparently incompatible previous views, it reconciles these views within a unifying framework.
ISSN:1568-5373
Περιλαμβάνει:Enthalten in: Journal of cognition and culture
Persistent identifiers:DOI: 10.1163/15685373-12340161