Bayesian Causal Inference: A Unifying Neuroscience Theory

Shams, Ladan; Beierholm, Ulrik

Abstract

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.

Citation

Shams, L., & Beierholm, U. (2022). Bayesian Causal Inference: A Unifying Neuroscience Theory. Neuroscience & Biobehavioral Reviews, 137, Article 104619. https://doi.org/10.1016/j.neubiorev.2022.104619

• 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
• DOI: https://doi.org/10.1016/j.neubiorev.2022.104619
• Public URL: https://durham-repository.worktribe.com/output/1212790