all subsequent models; see Methods), we asked whether participants displayed evidence of learning the probabilistic motor sequence. A commonly studied marker of motor skill learning is an exponential drop-off in movement time with trials practiced$^{26}$. We observed this drop-off, despite our task being probabilistic rather than deterministic. Learners exhibited large decreases in reaction time on average over the course of the first experiment. Across the initial structured graph stage, we observed mean reaction time decreasing by nearly 500 ms (Fig. 2A, black line. See Supplementary Figures 1 & 2 for additional information). The observed overall decrease in reaction time with training suggested general improvement in task performance. To explicitly evaluate learning based on graph structure, we tested for the cross-cluster surprisal effect , a previously reported measure of graph learning on modular graphs$^{4}$. Briefly, the cross-cluster surprisal effect is a significant increase in reaction time when a participant traverses an edge located between clusters, in comparison to when a participant traverses edges within clusters. Using a mixed effects model (see Methods), we observed a statistically significant increase in reaction time across edges that connected two clusters (Fig. 2A,B; linear mixed effects model; t ( 29 ) = 3 . 61 , p < 0 . 002, expected increase of 63.6 ms; 95% confidence interval: 29.07 to 98.10, see Supplementary Table 2). To provide an intuitive visualization of this finding, we first note that the graph was symmetric across the three clusters; because the starting position and traversal direction within the graph varied for each subject, the distinction between the three clusters was arbitrary when comparing across subjects. We therefore remapped each edge to the equivalent edge within a single canonical cluster, which visually highlights the clear difference in reaction times for between- versus within-cluster edges (Fig. 2C). The observed surprisal effect suggests that participants are sensitive to graph structure. However, an alternative explanation is that the surprisal effect reflects a difference in processing cost inherent to local repetitions associated with a random walk on the modular graph. To either support or dismiss this alternative explanation, we asked whether the surprisal effect would persist when we modified the walk to sample sparsely from each module in time, eliminating within-module repetitions of button presses. Prior work 4 suggests that learners are unlikely to show a surprisal effect when exposed to a Hamiltonian walk, where each node is only visited once per cycle. However, if clusters correspond to a learned feature of the graph, then we expect that learners first trained on a random walk (where we expect a surprisal effect) on a graph, and then exposed to a Hamiltonian walk (where we do not expect a surprisal effect) on the same graph, will continue to show the surprisal effect. In this experiment, subjects were first exposed to 1500 trials of a random walk on the modular graph, followed by 500 trials of a Hamiltonian walk on the same graph. We were interested to determine whether a subject’s surprisal effect in the first stage was correlated with their surprisal effect in the second stage. Using a mixed effects model, we found that our estimate for the 7/29