Cognitive Modeling of Complex Dynamic Environments
A cognitive architecture consists of a theory of those aspects of human cognition that are relatively constant and general across a wide range of situations, and a set of computational mechanisms to implement that theory and apply it to specific tasks and phenomena. Cognitive architectures are significant to the practice of HCI for two reasons. First, they present a more formal and systematic organization of the relevant aspects of human cognition than the usual collection of microtheories and isolated experimental results. Second, they provide a computational vehicle to apply that knowledge to construct models of user interfaces that deliver precise predictions regarding all measurable aspects of human behavior.In this talk, I will describe the ACT-R cognitive architecture and its applications to a broad range of tasks. ACT-R is a hybrid architecture that combines a symbolic production system with a subsymbolic, neural-like layer that optimizes itself to the statistical structure of the environment. I will introduce an ACT-R model of a basic paradigm of implicit learning, describe its implications for the learning of location sequences in menu interfaces and discuss its application to the modeling of complex dynamic environments.