Canonical behavior patterns

A common problem in many areas of behavioral research is the analysis of the large volume of protocol data recorded during the execution of tasks. This dissertation describes a new automated method of protocol analysis to find canonical behaviors—a small subset of behavior protocols that are most representative of the full data set. The method I have developed takes advantage of recent algorithmic developments in pattern recognition. By adapting these methods to the analysis of behavior protocols, I provide a new tool for analysts working with large datasets that are infeasible to study using current methods. The method I propose can also be used as an important complement to existing sequential protocol analysis techniques, by allowing researchers to build their models based on a few highly representative samples. The contributions of this dissertation include the adaptation of the method to the analysis of behavior protocols: the development of similarity measures appropriate to behavior protocols: an extension of the method to work in oriented topologies; and a demonstration of the method's utility in real-world problem domains, particularly web browsing and driving.