Cognitive Perspectives on Context-based Decisions and Explanations

In this paper we and Cognitive Science, there is a pervasive idea that argue that explanations, while not always perfectly accurate humans employ mental representations in order to in regards to reality (due to the existence of hidden factors), navigate the world and make predictions about outcomes are best structured and involve the same conceptual basis as of future actions. By understanding how decisions, and that when trying to understand an explanation these representational structures work, we not only we do so by simulating the decision-making process through understand more about human cognition but also the explanation provided. In other words, what makes a good gain a better understanding for how humans rationalise explanation of an agent-based action is that it presents us with and explain decisions. This has an influencing a reasoning structure that we can follow and relate to our own effect on explainable AI, where the goal is to decision-making processes. It is thus imperative that the explanations provide explanations of computer decision-making provided by artificial agents, in the context of XAI, for a human audience. We show that the Contextual not only provide deliberations that we can follow, but more Importance and Utility method for XAI share importantly provide them in a conceptual framework which an overlap with the current new wave of actionoriented facilitates retreading the deliberation and is context-sensitive.