  The mental models that humans form of other agents---encapsulating human
beliefs about agent goals, intentions, capabilities, and more---create an
underlying basis for interaction. These mental models have the potential to
affect both the human's decision making during the interaction and the human's
subjective assessment of the interaction. In this paper, we surveyed existing
methods for modeling how humans view robots, then identified a potential method
for improving these estimates through inferring a human's model of a robot
agent directly from their actions. Then, we conducted an online study to
collect data in a grid-world environment involving humans moving an avatar past
a virtual agent. Through our analysis, we demonstrated that participants'
action choices leaked information about their mental models of a virtual agent.
We conclude by discussing the implications of these findings and the potential
for such a method to improve human-robot interactions.
