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.
[1]
David S. Johnson,et al.
Computers and Intractability: A Guide to the Theory of NP-Completeness
,
1978
.
[2]
Ali Shokoufandeh,et al.
Finding canonical behaviors in user protocols
,
2009,
CHI.
[3]
Dario D. Salvucci.
Modeling Driver Behavior in a Cognitive Architecture
,
2006,
Hum. Factors.
[4]
Allen Newell,et al.
The psychology of human-computer interaction
,
1983
.
[5]
Ali Shokoufandeh,et al.
Canonical Patterns of Oriented Topologies
,
2010,
2010 20th International Conference on Pattern Recognition.
[6]
John R. Anderson,et al.
Eye tracking the visual search of click-down menus
,
1999,
CHI '99.
[7]
David S. Johnson,et al.
Computers and In stractability: A Guide to the Theory of NP-Completeness. W. H Freeman, San Fran
,
1979
.
[8]
Ali Shokoufandeh,et al.
Canonical subsets of image features
,
2008,
Comput. Vis. Image Underst..