dependent on reaction time across the entire graph rather than between different classes of nodes. We also note that excluding priming effects may be overly conservative, given that they may serve as a local cue to graph organization. Fifth and finally, our study does not address the question of how the exploration of graph structures in real stimuli is instantiated in the brain. For future work, it would interesting to consider recent evidence that co-occurrence of visual stimuli leads to increasingly similar neural representations in particular areas of human neocortex$^{40,41}$, and that these same areas can encode associative distances between objects$^{3,42}$. These data suggest that an understanding of the sensitivity to the topological properties of graph structures may have important implications in future for an understanding of neural coding. In conclusion, we note that our results highlight the importance of topological structure in learning from a complex environment. We first demonstrate that learning of higher-level statistics operates in the context of an SRT task. Related previous paradigms instead focused on perceptual learning tasks without this complex motor component. Thus, our results suggest that graph-based statistical learning mechanisms are unlikely to be modality-specific. Second, we have examined the impact of systematic differences in graph organization on learning. We find significant differences in subject performance on different graph types, despite identical numbers of stimuli and possible transitions in each graph. In particular, subjects show overall faster reaction times on sequences drawn from the modular graph. Lastly, we begin to explore why sequences drawn from these graphs may be easier or harder to learn, based on node-level statistics that topologically classify the node in relation to both its immediate neighbors and its global role within the graph. Understanding the degree to which these results generalize to other graph structures remains an important direction for future research, as well as understanding whether these results can provide insight on the organization of naturally occurring complex systems. Additionally, our current study does not address the impact of long-term learning on the surprisal effect. Whether the increase in RT persists or disappears as learners become better trained on the task may help distinguish between competing causes. All participants provided informed consent as specified by the Institutional Review Board (IRB) of the University of Penn- sylvania, and study methods and experimental protocols were approved by the IRB. We recruited 381 unique participants to complete our study on Amazon’s Mechanical Turk, an online marketplace for crowdsourced work. Worker IDs were used to exclude any duplicate participants, both within and between the three experiments. The entire sample included 109 participants for the first experiment, 59 participants for the second experiment, and 213 participants for the third experiment. No statistical Methods Participants 19/29