Code and dataset for neural dynamics of causal inference in the macaque frontoparietal circuit

Natural perception relies inherently on inferring causal structure in the environment. However, the neural mechanisms and functional circuits essential for representing and updating the hidden causal structure and corresponding sensory representations during multisensory processing are unknown. To address this, monkeys were trained to infer the probability of a potential common source from visual and proprioceptive signals based on their spatial disparity in a virtual reality system. The proprioceptive drift reported by monkeys demonstrated that they combined previous experience and current multisensory signals to estimate the hidden common source and subsequently updated the causal structure and sensory representation. Single-unit recordings in premotor and parietal cortices revealed that neural activity in the premotor cortex represents the core computation of causal inference, characterizing the estimation and update of the likelihood of integrating multiple sensory inputs at a trial-by-trial level. In response to signals from the premotor cortex, neural activity in the parietal cortex also represents the causal structure and further dynamically updates the sensory representation to maintain consistency with the causal inference structure. Thus, our results indicate how the premotor cortex integrates previous experience and sensory inputs to infer hidden variables and selectively updates sensory representations in the parietal cortex to support behavior. This dynamic loop of frontal-parietal interactions in the causal inference framework may provide the neural mechanism to answer long-standing questions regarding how neural circuits represent hidden structures for body awareness and agency.

自然感知本质上依赖于对环境中因果结构的推断。然而，在多感官加工（multisensory processing）过程中，用于表征和更新隐藏因果结构以及对应感觉表征的神经机制与功能环路仍未明确。为解决这一科学问题，研究人员训练猴子在虚拟现实（virtual reality）系统中，基于视觉与本体感受信号的空间差异，推断潜在共同来源的概率。猴子报告的本体感受漂移现象表明，它们会整合既往经验与当前多感官信号，以估计隐藏的共同来源，并随后更新因果结构与感觉表征。对运动前皮层（premotor cortex）和顶叶皮层（parietal cortex）的单细胞记录（single-unit recordings）结果显示，运动前皮层的神经活动表征了因果推断的核心计算过程，即在逐试次层面整合多种感觉输入的可能性估计与更新。响应于运动前皮层的信号，顶叶皮层的神经活动同样表征了因果结构，并进一步动态更新感觉表征以与因果推断结构保持一致。综上，本研究结果揭示了运动前皮层如何整合既往经验与感觉输入，以推断隐藏变量，并选择性更新顶叶皮层的感觉表征以支持行为表现。这种基于因果推断框架的额顶叶交互动态环路，或许能够为解答长期存在的科学疑问提供神经机制——即神经环路如何表征与身体觉知（body awareness）和能动性（agency）相关的隐藏结构。