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A computational perspective on faith: religious reasoning and Bayesian decision

Rigoli, F. ORCID: 0000-0003-2233-934X (2020). A computational perspective on faith: religious reasoning and Bayesian decision. Religion, Brain and Behavior, 11(2), pp. 147-164. doi: 10.1080/2153599x.2020.1812704


Religious reasoning (the processes through which religious beliefs are formed) has been investigated by two different approaches. First, explanation theories portray religious reasoning as arising for explaining salient aspects of reality. Second, motivation theories interpret religious reasoning as driven by other motives, for example fostering community bonding. Both approaches have provided fundamental insight, yet whether they can be reconciled remains unclear. To address this, I propose a unifying computational theory of religious reasoning expressed in mathematical terms. Although a mathematical approach has been rarely applied to study religion, its advantage is describing a phenomenon clearly and formally. Relying on a Bayesian decision framework, the model comprises three key elements: prior beliefs, novel evidence, and utility. The first two describe the processes classically interpreted by explanation theories, while utility captures phenomena highlighted by motivation theories. By reconciling explanation and motivation theories, this proposal offers a unifying computational picture of religious reasoning.

Publication Type: Article
Additional Information: This is an Accepted Manuscript of an article published by Taylor & Francis in Religion, Brain and Behavior on 21 Sep 2020, available online:
Publisher Keywords: Bayesian, computational modeling, motivation, religion, decision theory
Subjects: B Philosophy. Psychology. Religion > BF Psychology
B Philosophy. Psychology. Religion > BL Religion
H Social Sciences > HB Economic Theory
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments: School of Health & Psychological Sciences > Psychology
SWORD Depositor:
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Text - Accepted Version
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