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Bayesian Causal Inference: A Unifying Neuroscience Theory

Shams, Ladan; Beierholm, Ulrik

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Ladan Shams


Understanding of the brain and the principles governing neural processing requires theories that are parsimonious, can account for a diverse set of phenomena, and can make testable predictions. Here, we review the theory of Bayesian causal inference, which has been tested, refined, and extended in a variety of tasks in humans and other primates by several research groups. Bayesian causal inference is normative and has explained human behavior in a vast number of tasks including unisensory and multisensory perceptual tasks, sensorimotor, and motor tasks, and has accounted for counter-intuitive findings. The theory has made novel predictions that have been tested and confirmed empirically, and recent studies have started to map its algorithms and neural implementation in the human brain. The parsimony, the diversity of the phenomena that the model has explained, and its illuminating brain function at all three of Marr’s levels of analysis make Bayesian causal inference a strong neuroscience theory. This also highlights the importance of collaborative and multi-disciplinary research for the development of new theories in neuroscience.


Shams, L., & Beierholm, U. (2022). Bayesian Causal Inference: A Unifying Neuroscience Theory. Neuroscience & Biobehavioral Reviews, 137, Article 104619.

Journal Article Type Article
Acceptance Date Mar 10, 2022
Online Publication Date May 4, 2022
Publication Date 2022-06
Deposit Date Mar 15, 2022
Publicly Available Date Jun 28, 2022
Journal Neuroscience and Biobehavioral Reviews
Print ISSN 0149-7634
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 137
Article Number 104619


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Copyright Statement
This is an open access article distributed under the terms of the Creative Commons CC-BY license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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