ABSTRACT
Simple statistical analyses and machine learning algorithms are used to solve practical information access problems. In addition these same statistical properties of objects in the world constrain human performance. Perceptual and cognitive systems, including the developmental and learning mechanisms that shape them during the lifespan, are the result of evolution by natural selection. The chapter provides a Bayesian theoretical framework that makes explicit the relationship between the statistical properties of the environment, the evolving genome, and the design of perceptual and cognitive systems. It describes the connections between the Bayesian framework and other theoretical approaches. Information about the structure of a causal system can come in the form of observational data - random samples of the system’s autonomous behavior - or interventional data -samples conditioned on the particular values of one or more variables that have been experimentally manipulated.
