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Hierarchical Bayesian narrative-making under variable uncertainty

Published online by Cambridge University Press:  08 May 2023

Alex Jinich-Diamant Open the ORCID record for Alex Jinich-Diamant [Opens in a new window]  and
Leonardo Christov-Moore Open the ORCID record for Leonardo Christov-Moore [Opens in a new window]
Alex Jinich-Diamant
Affiliation:
Department of Anesthesiology, Department of Cognitive Science, University of California San Diego, La Jolla, CA 92093-0515, USA aljinich@ucsd.edu Multiscale Complexity Institute, Centro Oaxaqueño de la Conciencia, 68287 Oaxaca, Mexico
Leonardo Christov-Moore
Affiliation:
Multiscale Complexity Institute, Centro Oaxaqueño de la Conciencia, 68287 Oaxaca, Mexico Institute for Advanced Consciousness Studies, Santa Monica, CA 90403, USA. christovmoore@gmail.com https://scholar.google.com/citations?user=PHJcx1IAAAAJ&hl=en
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Abstract

While Conviction Narrative Theory correctly criticizes utility-based accounts of decision-making, it unfairly reduces probabilistic models to point estimates and treats affect and narrative as mechanistically opaque yet explanatorily sufficient modules. Hierarchically nested Bayesian accounts offer a mechanistically explicit and parsimonious alternative incorporating affect into a single biologically plausible precision-weighted mechanism that tunes decision-making toward narrative versus sensory dependence under varying uncertainty levels.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 46 , 2023 , e97
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Copyright
Copyright © The Author(s), 2023. Published by Cambridge University Press

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