Our research focuses on how structured visual information is acquired and the conversion of this information into sophisticated internal representations for controlling behavior. We use an integrated approach with three main components: human psychophysical and learning experiments, computational modeling of learning, and multi-electrode recording from behaving animals. The recurrent theme of our work is the pursuit of a statistically based and biologically sound framework to link low-level visual mechanisms (e.g., adaptation) with the development and learning of higher level complex features and constancies for efficient visual representations of objects and scenes.