relation between reaction time and these statistics specifically in the random graph exposures from the first and third experiment, where we observed the greatest variability in degree and betweenness centrality over nodes. In all cases, we regressed out the visits to a node, to separate the influence of network topology from the influence of increased exposure. We found that node degree highly predicted the mean response time on a node (Fig. 4B,D; Kendall’s $_{t}$= 0 . 072 , n = 2655 , p < 0 . 001), as did node betweenness centrality (Fig. 4C,E; Kendall’s $_{t}$= 0 . 044 , n = 2655 , p < 0 . 001). These results indicate that not only does meso-scale graph organization affect learnability, but so do smaller scale topological features quantifying the number of edges in a node’s immediate neighborhood, and larger scale topological features quantifying a node’s role in long-distance traversals through the graph. Intriguingly, we observed an inverted relationship when we refrained from regressing out the number of visits to a node, a fact that highlights the complex relationship between topology and learnability. (For full results, and additional findings related to other graph metrics, see Supplementary Fig. 4-7. Also note that degree and node betweenness centrality were correlated in the graphs analyzed in this experiment – Kendall’s $_{t}$= 0 . 669 , n = 2655 , p < 0 . 001 – indicating that nodes with dense local connectivity also play an important role in long-distance traversals.) In turning to a discussion of our results, we begin by grounding our experimental setup and findings in the context of prior literature. Then in later sections, we turn our attention to a discussion of more specific implications of graph architecture for learning. In this study, we capitalize on a rich history of using motor response times as a learning measure (e.g., Cleeremans & McClelland $^{11}$; for reviews see: Cleeremans et al. $^{28}$, Robertson $^{29}$). While those studies (and much of the artificial grammar learning work since Reber $^{30}$) have clear parallels to the present findings, our experiments diverge in the fundamental question they address. While we similarly generate sequences by “walking” along the edges of a graph, we additionally systematically manipulate the topological properties of the graph underlying motor responses. In contrast to the bulk of the artificial grammar learning literature, which largely relies on an arbitrary finite-state grammar, we instead apply organizational principles informed by graph theory to study biases in human learning. In the current study, we employed tools from the field of network science to determine whether and how graph structure influences sequence learning. More specifically, our experiment built upon previous work demonstrating that modular graph organization influences visual statistical learning$^{3,4}$. We extended this line of inquiry to the motor domain by assigning unique button presses to nodes of a modular graph, generating sequences via a random walk on that graph, and asking whether learners exhibit similar behavioral effects at the boundaries between clusters of nodes. Indeed, we found that learners displayed a sharp increase in reaction time when transitioning from one cluster of button presses to another, indicating that they developed implicit expectations about the underlying topology of incoming sequential input. The sum of these results indicates that 12/29