Modeling strategy differences in a complex fault-finding task

This paper presents multiple models of strategies that people may apply to find faults in a complex circuit. Previous researchers have modeled how, when, and what is learned in a simple fault-finding task. Furthermore, they started to explore individual differences in strategies. We continued modeling multiple strategies for tasks with higher complexity, moving from a simple circuit to a more complex circuit with 5 subcircuits. The multiple strategies that participants may use are implemented in novel approach that uses hierarchical task analysis, the KLM, and the ACT-R learning equations. We compared the time spent to finish tasks in Session 1 and 5 between participants and each model. This research provides insights into why we sometimes failed to predict behaviors well—it is not the problem of strategies being modeled but the variations and range of participants’ strategies.