Cortical Hierarchies Perform Bayesian Causal Inference in Multisensory Perception

By Tim Rohe, Uta Noppeney. 2015

336 citations recorded by the source corpus.

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.

Subfields: Cognitive Neuroscience, Experimental and Cognitive Psychology, Sensory Systems

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Citations in the corpus

Sensory reliability shapes perceptual inference via two mechanisms. (2015, background): “A recent fMRI study suggests that a cortical hierarchy performs Bayesian CI for spatial localization by representing multiple spatial estimates, which are closely related to forced-fusion (e.g., ŜAV,C¼1), fullsegregation (e.g., ŜAV,C¼2), and Bayesian CI (e.g., ŜA; Rohe & Noppeney, 2015).”
Multisensory Causal Inference in the Brain (2015, background): “As shown in a new study by Rohe and Noppeney, important insights can be obtained by combining computational models with carefully crafted analysis of brain activity [2].”
The Brain’s Tendency to Bind Audiovisual Signals Is Stable but Not General (2016, positive): “…make explicit predictions about the locations of unisensory and multisensory processing areas in the brain, one recent study using functional MRI and a spatial localization task made progress in this area by mapping estimates from this model onto different cortical areas (Rohe & Noppeney, 2015a).”
The Curious Incident of Attention in Multisensory Integration: Bottom-up vs. Top-down (2016, positive): “The development of new theoretical and mathematical approaches (e.g. Bayesian causal inference, Rohe and Noppeney, 2015a) will help us with the interpretation of these neuronal effects.”
The COGs (context, object, and goals) in multisensory processing (2016, background): “Approaches such as predictive coding and Bayesian models (Fries 2005; Schroeder et al. 2010; Summerfield and Egner 2009; Summerfield and de Lange 2014; Rohe and Noppeney 2015) have highlighted the importance of this form of top-down control, based on the context, or the “immediate situation in which”
Distinct Computational Principles Govern Multisensory Integration in Primary Sensory and Association Cortices. (2016, positive): “A recent model-based fMRI study showed that IPS3–4 is more likely to encode spatial estimates formed by Bayesian causal inference [33] than by traditional forced fusion [1, 3] or full segregation models.”
Biases in Visual, Auditory, and Audiovisual Perception of Space (2015, methodology): “Moreover, a human neuroimaging study recently provided further support for the Bayesian Causal Inference being carried out in human brain in the spatial localization task, using the same task used in current study [37].”