Automated GOMS-to-ACT-R model generation (Best applied paper)

We describe a system, G2A, that produces ACT-R models from GOMS models containing hierarchical methods, visual and memory stores, and control constructs. Because GOMS is a more abstract formalism than ACT-R, a single GOMS operator might be plausibly translated in different ways into ACT-R productions (e.g., a GOMS Look-for operator might be carried out by different visual search strategies in ACT-R). Given a GOMS model, G2A generates and evaluates alternative ACT-R models by systematically varying the mapping of GOMS operators to ACT-R productions. In experiments with a text editing task, G2A produces ACT-R models with predictions that are within 5% of GOMS model predictions. In the same domain, G2A also generates ACT-R models that give good predictions of overall task duration for actual users (within 2% error), though the models are much less accurate at a detailed level.