For many cognitive scientists, psychologists, and philosophers of mind, the best current theory of cognition holds that thinking is in some sense computation “in some sense,” because that core idea can and has been elaborated in a number of different ways that are or at least seem to be incompatible in at least some respects. In Unifying the Mind: Cognitive Representations as Graphical Models (MIT Press, 2014), David Danks proposes a version of this basic theory that links the mind closely with the computational framework used in machine learning: the idea that thinking involves manipulation of symbols encoded as graphical models. Danks, who is Professor of Philosophy and Psychology at Carnegie Mellon University, argues that graphical models provide a unifying explanation of why we are able to move smoothly between different cognitive processes and why we are able to focus on features of situations that are relevant to our goals. While the book includes the mathematics behind graphical models, Danks explains his proposal in accessible yet precise terms for the non-mathematically trained reader. He discusses how graphical models work in causal reasoning, categorization, and other processes, how his view is related to more familiar cognitive frameworks, and some implications of his view for modularity and other traditional debates.