We live in a world consisting of concrete experiences, yet we appear to form abstractions that transcend the details of our experiences. In this contribution, we argue that the abstract nature of our thought is overstated and that our representations are inherently bound to the examples we experience during learning. We present three lines of related research to support this general point. The first line of research suggests that there are no separate learning systems for acquiring mental rules and storing exceptions to these rules. Instead, both items types share a common representational substrate that is grounded in experienced training examples. The second line of research suggests that representations of abstract concepts, such as same and different that can range over an unbounded set of stimulus properties, are rooted in experienced examples coupled with analogical processes. Finally, we consider how people perform in dynamic decision tasks in which short- and long-term rewards are in opposition. Rather than invoking explicit reasoning processes and planning, people's performance is best explained by reinforcement learning procedures that update estimates of action values in a reactive, trial-by-trial fashion. All three lines of research implicate mechanisms of thought that are capable of broad generalization, yet inherently local in terms of the procedures used for updating mental representations and planning future actions. We end by considering the benefits of designing systems that operate according to these principles.