Causal Inference in Multisensory Perception
Left: One cause can be responsible for both cues. In this case the visually perceived position xV will be the common position s perturbed by visual noise with width σV and the auditory perceived position will be the common position perturbed by auditory noise with width σA. Right: Alternatively, two distinct causes may be relevant, decoupling the problem into two independent estimation problems. The causal inference model infers the probability of a causal structure with a common cause (left, C = 1) versus the causal structure with two independent causes (right, C = 2) and then derives optimal predictions from this. We introduce a single variable C which determines which sub-model generates the data.