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  1. The Computations Underlying Religious Conversion: A Bayesian Decision Model.Francesco Rigoli - 2023 - Journal of Cognition and Culture 23 (1-2):241-257.
    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 (...)
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  • Guess who? Identity attribution as Bayesian inference.Francesco Rigoli - forthcoming - Philosophical Psychology.
    An influential argument is that mental processes can be explained at three different levels of analysis: the functional, algorithmic, and implementation level. Identity attribution (the process whereby an identity is attributed to another individual or to the self) has been rarely explored at the functional level. To address this, here I propose a theory of identity attribution grounded on Bayesian inference, being the latter a well-established functional perspective in cognitive science. The theory posits that an identity is inferred based on (...)
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  • Parameter Inference for Computational Cognitive Models with Approximate Bayesian Computation.Antti Kangasrääsiö, Jussi P. P. Jokinen, Antti Oulasvirta, Andrew Howes & Samuel Kaski - 2019 - Cognitive Science 43 (6):e12738.
    This paper addresses a common challenge with computational cognitive models: identifying parameter values that are both theoretically plausible and generate predictions that match well with empirical data. While computational models can offer deep explanations of cognition, they are computationally complex and often out of reach of traditional parameter fitting methods. Weak methodology may lead to premature rejection of valid models or to acceptance of models that might otherwise be falsified. Mathematically robust fitting methods are, therefore, essential to the progress of (...)
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