To integrate or not to integrate: Temporal dynamics of hierarchical Bayesian causal inference

To form a percept of the environment, the brain needs to solve the binding problem—inferring whether signals come from a common cause and are integrated or come from independent causes and are segregated. Behaviourally, humans solve this problem near-optimally as predicted by Bayesian causal inference; but the neural mechanisms remain unclear. Combining Bayesian modelling, electroencephalography (EEG), and multivariate decoding in an audiovisual spatial localisation task, we show that the brain accomplishes Bayesian causal inference by dynamically encoding multiple spatial estimates. Initially, auditory and visual signal locations are estimated independently; next, an estimate is formed that combines information from vision and audition. Yet, it is only from 200 ms onwards that the brain integrates audiovisual signals weighted by their bottom-up sensory reliabilities and top-down task relevance into spatial priority maps that guide behavioural responses. As predicted by Bayesian causal inference, these spatial priority maps take into account the brain’s uncertainty about the world’s causal structure and flexibly arbitrate between sensory integration and segregation. The dynamic evolution of perceptual estimates thus reflects the hierarchical nature of Bayesian causal inference, a statistical computation, which is crucial for effective interactions with the environment.

为形成对环境的知觉表征，大脑需要解决绑定问题（binding problem）——即推断信号源自共同诱因并被整合，还是源自独立诱因并被分离处理。行为层面上，人类解决该问题的表现接近贝叶斯因果推断（Bayesian causal inference）的最优预测，但背后的神经机制仍不明晰。本研究结合贝叶斯建模、脑电图（electroencephalography, EEG）与多元解码技术，在视听空间定位任务中发现，大脑通过动态编码多组空间估计值来实现贝叶斯因果推断。最初，大脑会独立估计听觉与视觉信号的位置；随后，会形成一组整合视听信息的联合估计值。然而，直至200毫秒之后，大脑才会将视听信号依据其自下而上的感觉可靠性与自上而下的任务相关性进行加权整合，形成指导行为反应的空间优先级图谱（spatial priority maps）。正如贝叶斯因果推断所预测的那样，这些空间优先级图谱会考量大脑对世界因果结构的不确定性，并在感觉整合与分离处理之间灵活仲裁决策。因此，知觉估计的动态演化过程，反映了贝叶斯因果推断——一种对实现与环境的有效交互至关重要的统计计算方法——的层级性本质。