Cortical Hierarchies Perform Bayesian Causal Inference in Multisensory Perception

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.The human brain uses Bayesian Causal Inference to integrate and segregate information by encoding multiple estimates of spatial relationships at different levels of the auditory and visual processing hierarchies. Read the accompanying Primer.Author Summary: How can the brain integrate signals into a veridical percept of the environment without knowing whether they pertain to same or different events? For example, I can hear a bird and I can see a bird, but is it one bird singing on the branch, or is it two birds (one sitting on the branch and the other singing in the bush)? Recent studies demonstrate that human observers solve this problem optimally as predicted by Bayesian Causal Inference; yet, the neural mechanisms remain unclear. By combining psychophysics, Bayesian modelling, functional magnetic resonance imaging (fMRI), and multivariate decoding in an audiovisual localization task, we show that Bayesian Causal Inference is performed by a neural hierarchy of multisensory processes. 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 world’s causal structure is taken into account and sensory signals are combined as predicted by Bayesian Causal Inference.