Abstract
Our senses are constantly bombarded with myriads of diverse signals. Transforming this sensory cacophony into a coherent percept of our environment relies on solving two computational challenges: First, we need to solve the causal inference problem - deciding whether signals come from a common cause and thus should be integrated, or come from different sources and be treated independently. Second, when there is a common cause, we should integrate signals across the senses weighted in proportion to their sensory precisions. I discuss recent research at the behavioural, computational and neural systems level investigating how the brain combines sensory signals in the face of uncertainty about the world’s causal structure. Our results show that the brain constructs a multisensory representation of the world approximately in line with Bayesian Causal Inference.