Dissociable Neural Systems Support the Learning and Transfer of Hierarchical Control Structure.

Humans can draw insight from previous experiences in order to quickly adapt to novel environments that share a common underlying structure. Here we combine functional imaging and computational modeling to identify the neural systems that support the discovery and transfer of hierarchical task structure. Human subjects (male and female) completed multiple blocks of a reinforcement learning task that contained a global hierarchical structure governing stimulus-response action mapping. First, behavioral and computational evidence showed that humans successfully discover and transfer the hierarchical rule structure embedded within the task. Next, analysis of fMRI BOLD data revealed activity across a frontal-parietal network that was specifically associated with the discovery of this embedded structure. Finally, activity throughout a cingulo-opercular network supported the transfer and implementation of this discovered structure. Together, these results reveal a division of labor in which dissociable neural systems support the learning and transfer of abstract control structures. Significance Statement A fundamental and defining feature of human behavior is the ability to generalize knowledge from the past in order to support future action. Although the neural circuits underlying more direct forms of learning have been well established over the last century, we still lack a solid framework from which to investigate more abstract, higher order human learning and knowledge generalization. We designed a novel behavioral paradigm in order to specifically isolate a learning process in which previous knowledge, rather than directly indicating the correct action, instead guides the search for the correct action. Moreover, we identify that this learning process is achieved via the coordinated and temporally specific activity of two prominent cognitive control brain networks.