Departments of Psychology, BioEngineering, and Neuroscience Interdepartmental Program, University of California, Los Angeles, USA.
Department of Psychology, University of Durham, UK.
Neurosci Biobehav Rev. 2022 Jun;137:104619. doi: 10.1016/j.neubiorev.2022.104619. Epub 2022 Mar 21.
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 theory 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.
理解大脑和神经处理的原理需要简洁、能够解释多种现象并能做出可检验预测的理论。在这里，我们回顾了贝叶斯因果推理理论，该理论已经在人类和其他灵长类动物的各种任务中经过了测试、改进和扩展，由几个研究小组完成。贝叶斯因果推理是规范性的，它已经解释了人类在大量任务中的行为，包括单感觉和多感觉感知任务、感觉运动和运动任务，并解释了违反直觉的发现。该理论提出了新颖的预测，这些预测已经通过实证检验和确认，最近的研究已经开始在人类大脑中映射其算法和神经实现。该理论的简洁性、所解释现象的多样性以及在马吕斯三个分析层面上的启发性脑功能，使贝叶斯因果推理成为一种强有力的神经科学理论。这也强调了协作和多学科研究对于神经科学新理论发展的重要性。
