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Many robotics applications and scenarios that involve interaction with humans are safety or performance critical. A natural path to assessing such notions is to include a cognitive model describing typical human behaviors into a larger modeling context. In this work, we set out to investigate a combination of such a model with formal verification. We present a general and flexible framework utilizing methods from probabilistic model checking and discuss current pitfalls. We start from information about typical behavior, obtained from generalizing specific scenarios by the usage of inverse reinforcement learning.