Cortical Hierarchies Perform Bayesian Causal Inference in Multisensory Perception

TLDR

The brain must integrate sensory signals from a common source while segregating independent ones, a challenge known as the causal inference problem that humans solve optimally but whose neural basis remains unexplored. We show that a cortical hierarchy performs Bayesian causal inference, with auditory and visual areas encoding segregation, posterior intraparietal sulcus enforcing forced fusion, and anterior intraparietal sulcus integrating signals while accounting for causal uncertainty. These results reveal the hierarchical operations of multisensory perception in human neocortex and demonstrate how the brain combines information when causal structure is uncertain.

Abstract

To form a veridical percept of the environment, the brain needs to integrate sensory signals from a common source but segregate those from independent sources. Thus, perception inherently relies on solving the "causal inference problem." Behaviorally, humans solve this problem optimally as predicted by Bayesian Causal Inference; yet, the underlying neural mechanisms are unexplored. Combining psychophysics, Bayesian modeling, functional magnetic resonance imaging (fMRI), and multivariate decoding in an audiovisual spatial localization task, we demonstrate that Bayesian Causal Inference is performed by a hierarchy of multisensory processes in the human brain. At the bottom of the hierarchy, in auditory and visual areas, location is represented on the basis that the two signals are generated by independent sources (= segregation). At the next stage, in posterior intraparietal sulcus, location is estimated under the assumption that the two signals are from a common source (= forced fusion). Only at the top of the hierarchy, in anterior intraparietal sulcus, the uncertainty about the causal structure of the world is taken into account and sensory signals are combined as predicted by Bayesian Causal Inference. Characterizing the computational operations of signal interactions reveals the hierarchical nature of multisensory perception in human neocortex. It unravels how the brain accomplishes Bayesian Causal Inference, a statistical computation fundamental for perception and cognition. Our results demonstrate how the brain combines information in the face of uncertainty about the underlying causal structure of the world.