In common parlance, the switch cost is an increase in response time when learners are prompted to switch tasks between trials, where a task might be reporting the color of a stimulus, or the magnitude of a number. Typically each task is conceived of as having its own stimulus-response mapping, which is similar to the discrete set of responses anticipated within each of our clusters. While many studies have demonstrated the persistence of the switch cost even when subjects anticipate task order, a few studies have importantly shown that a switch cost is still observed when participants are led to implicitly learn a task sequence$^{33,34}$, similar to the implicit presentation of modular structure in our current study. Moreover, the relationship between task and sequence has been shown to be hierarchical$^{35}$, where both task and sequence interact with one another. This observation provides a link to our current work where not only do the two interact, but the task (in the form of a cluster) is defined by the sequence. In many real-world situations, tasks do not have explicitly defined subtasks nor clear boundaries between those subtasks, and natural divisions are only learned through experience. Thus, a comprehensive exploration of the flexible interplay between task and task sequence might be a promising direction for future research. One final framing of interest relates to the dependence of RT on the local topology as measured by the degree and betweenness centrality of the random graph. Both of these metrics capture the distinctiveness of a node’s role within the graph. The degree reflects the number of accessible nodes from a single node; the betweenness centrality reflects the likelihood that a node will be necessary to traverse when moving between any pair of nodes in the graph. Importantly, the modular graph also has important local structure, and the nodes connecting two clusters in the modular graph (boundary nodes) serve a distinct role from the nodes existing within a cluster. One possibility is that a similar mechanism underlies the modulation of RT by the cluster structure and the modulation of RT by the degree and betweenness centrality structure. For instance, boundary nodes in the modular graph exhibit much higher betweenness centrality (0.22) than within-cluster nodes (0.04). And indeed, in both the random graphs and the modular graphs, higher betweenness centrality is significantly associated with higher RTs when differences in degree are accounted for. Thus one could conceive of the surprisal effect as a direct result of these graph properties. However, our current experiment does not allow us to further explore this relationship, as subjects only learned a single modular graph with two distinct classes of nodes. A potential future direction would be to explore whether this is a general relationship across other graphs with modular structure. The primary aim of this study was to examine differences in learners’ sensitivity to distinct graph structures, while holding constant the process through which these graphs were traversed (i.e., via a random walk). By exposing participants to modular, lattice, and random graphs, we sought to ascertain how higher-level structure might aid or impede motor skill acquisition. Given that modularity is an essential organizational principle underlying such varied systems as music 36 and social networks$^{21,37}$, 15/29