Cortical Hierarchies Perform Bayesian Causal Inference in Multisensory Perception

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 ps...

Causal Inference in Multisensory Perception

Perceptual events derive their significance to an animal from their meaning about the world, that is from the information they carry about their causes. The brain should thus be able to efficiently infer the causes underlying our sensory events. Here we use multisensory cue combination to study causal inference in perception. We formulate an ideal-observer model that infers whether two sensory ...

Humans ’ multisensory perception , from integration to segregation , follows Bayesian inference

Structure Inference for Bayesian Multisensory Perception and Tracking

We investigate a solution to the problem of multisensor perception and tracking by formulating it in the framework of Bayesian model selection. Humans robustly associate multi-sensory data as appropriate, but previous theoretical work has focused largely on purely integrative cases, leaving segregation unaccounted for and unexploited by machine perception systems. We illustrate a unifying, Baye...

Bayesian multisensory perception

We investigate a solution to the problem of multi-sensor perception by formulating it in the framework of Bayesian model selection. Humans robustly integrate and segregate multi-sensory data as appropriate, but previous theoretical work has focused largely on purely integrative cases, leaving segregation unaccounted for and unexploited by machine perception systems. We illustrate a unifying, pr...