A comprehensive method for comparing mental models of dynamic systems

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Explanation in Human-AI Systems: A Literature Meta-Review, Synopsis of Key Ideas and Publications, and Bibliography for Explainable AI (2019, background): “(Hilton, 1986, 1996; Prietula, Feltovich, and Marchak, 2000; Schaffernicht and Groesser, 2011; Tullio, Dey, Chalecki, and Fogarty, 2007)”
Metrics for Explainable AI: Challenges and Prospects (2018, methodology): “…noted in social studies as a method for comparing individuals' mental models of social groups (i.e., individuals and their inter-relations; Carley and Palmquist, 1992), and in operations research as a method for comparing mental models of dynamical systems (see Schaffernicht and Groesser, 2011).”
A Multidisciplinary Survey and Framework for Design and Evaluation of Explainable AI Systems (2018, background): “taining user trust over time. Time-based measurements, also referred to as dynamic measurements, allows designers to monitor explanation usability and effectiveness in various contexts and situations [50, 185]. For instance, Zhang et al. [222] explore the effect of intelligent system explanations in”