task, where each trial was drawn from a traversal through a graph. Each node represented a stimulus, and each edge represented a possible transition between two nodes. Based on prior work demonstrating learners’ sensitivity to higher-order statistics in SRT-like tasks$^{11}$, we hypothesized that learners would display sensitivity to graph structure as evidenced by a surprisal effect. Next, we systematically varied graph structure to examine the impact of graph topology on the acquisition of complex, multi-element motor sequences. We hypothesized that learners would display increasingly rapid execution of button presses when presented with modular graph structures in comparison to either random or lattice graphs. The predicted preference for modular graphs is based on evidence across disparate fields of scientific inquiry. Modular topologies are more frequently observed in real-world systems than either random or lattice-like topologies$^{23}$. Moreover, the clustering and hierarchy of modular graphs in natural systems can emerge in response to constraints on network wiring costs$^{24}$, and similar constraints on complexity may impact learnability itself. Indeed, we predicted that learning mechanisms should be tuned to statistical features of natural stimuli$^{25}$. Finally, we hypothesized that learning performance would vary over different regions of the graph based on both local and global properties. Local properties are those inherent in a single node and its immediate neighbors, and global properties encompass organizational features of the entire graph such as clustering or repeated structure. Together these properties can be used to assess learners’ sensitivity to variations in the graph across topological scales. Collectively, the results we report below suggest that learners extract and exploit the graph topology defining temporal sequences of stimuli, and that topological features impact speed of acquisition. Setup: We recruited 381 unique participants: 109 participants for a first experiment, 59 participants for a second experiment, and 223 participants for a third experiment. Subjects performed a self-paced SRT motor response task using a keyboard. Stimuli were five grey squares in a horizontal row; to indicate a target key or pair of keys that the subject was meant to press, the corresponding square(s) would be outlined in red (Fig. 1A). The squares corresponded spatially with keys ‘Space’, ‘H’, ‘J’, ‘K’ and ‘L’, such that the left square represented ‘Space’ and the right square represented ‘L’ (Fig. 1B). These keys were chosen so that the subject’s hand could ergonomically rest over all five keys at once. Sequence Generation: The order in which stimuli were presented to the subject was prescribed by either a random or a Hamiltonian walk on a graph of N = 15 nodes connected by E = 30 edges (Supplementary Table 1). Random Walk: For any two nodes that were not connected by an edge, the transition probability was equal to zero. For any two nodes that were connected by an edge, the transition probability was equal to 1 divided by the number of edges emanating from the pre-transition node. Hamiltonian Walk: A walk composed of Results 4/29