My interest in discussions about scientific modeling, idealization and scientific realism started with a coauthored paper “The Literalist Fallacy and the Free Energy Principle: Model-building, Scientific Realism and Instrumentalism”, published in the British Journal for the Philosophy of Science in 2025. The motivation for writing this paper was the observation that several philosophers of cognitive science were inferring the truth of instrumentalism (a version of scientific antirealism) from the fact that theoretical frameworks in neuroscience make use of idealizations. This inference is fallacious; or so we argued. We called it the literalist fallacy. The literalist fallacy is the fallacy of accepting or affirming instrumentalism based on the claim that the theoretical constructs of scientific models do not literally map onto real-world, target systems. However, the widespread use of idealization in science does not entail any kind of scientific antirealism. Therefore, even if models of the mind are both simplified and idealized, this does not stand in the way of defending a version of scientific realism in the sciences of the mind. This is one of the positions defended in The Idealized Mind.The Idealized Mind is written for methodological purposes in philosophy of cognitive science and the computational cognitive sciences. In the book, I seek to address a novel combination of topics across the philosophy of modeling (and philosophy of science more generally), philosophy of cognitive science, and computational cognitive science. There is a large literature within the philosophy of cognitive science and computational neuroscience that focuses on core issues such as neural representation, neural computation and the prospects of explanatory unification. Yet, these discussions hardly mention methodological issues concerning scientific modeling (and if and where they do, idealization takes a back seat, if it is given a seat at all). This is unfortunate because the cognitive sciences are predominantly a model-based science. Conversely, there is a fast-growing literature on scientific modeling in philosophy of science. However, here one finds no discussion of cognitive science—the focus is mostly on examples from physics. This book rectifies this by bringing these two fields of research together. My hope is that integrating the literature on idealization and scientific modeling with current work in cognitive science will foster a new scientific modeling paradigm for cognitive science and its philosophy: a paradigm where all the central issues about scientific modeling are at the heart of research in cognitive science. I argue that insights about the importance of idealization in science, and how the rampant use of idealization relates to realism about scientific theories, have direct implications for work in cognitive science, especially concerning the status and standing of neural representation, neural computation, and the prospects of explanatory unification.