Cognitive neuroscience has developed many approaches to the study of learning that might be useful to functionally oriented researchers, including those from a relational frame theory (RFT) perspective. We focus here on two examples. First, cognitive neuroscience often distinguishes between habit and goal-directed reinforcement learning, in which only the latter is sensitive to proximal changes in behavior-environment contingencies. This distinction is relevant to RFT’s original concerns about how rule-based processes can sometimes render an individual’s behavior maladaptive to changing circumstances. Second, the discovery of neurophysiological structures associated with fear extinction and generalization can potentially yield new insights for derived relational responding research. In particular, we review how such work not only informs new ways of modifying the functions transformed in derived relational responding, but also new ways of measuring derived relational responding itself. Overall, therefore, existing conceptual and methodological advances in the cognitive neuroscience literature addressing learning appear to generate functionally interesting predictions related to RFT that might not have surfaced from a traditional functional analysis of behavior.