Generative Probabilistic Modeling: Understanding Causal Sensorimotor Integration

This chapter argues that many aspects of human perception are best explained by adopting a modeling approach in which experimental subjects are assumed to possess a full generative probabilistic model of the task they are faced with, and that they use this model to make inferences about their environment and act optimally given the information available to them. It applies this generative modeling framework in two diverse settings- concurrent sensory and motor adaptation, and multisensory oddity detection-and shows, in both cases, that the data are best described by a full generative modeling approach.