  When human cognition is modeled in Philosophy and Cognitive Science, there is
a pervasive idea that humans employ mental representations in order to navigate
the world and make predictions about outcomes of future actions. By
understanding how these representational structures work, we not only
understand more about human cognition but also gain a better understanding for
how humans rationalise and explain decisions. This has an influencing effect on
explainable AI, where the goal is to provide explanations of computer
decision-making for a human audience. We show that the Contextual Importance
and Utility method for XAI share an overlap with the current new wave of
action-oriented predictive representational structures, in ways that makes CIU
a reliable tool for creating explanations that humans can relate to and trust.
