that the division between the two sections of the experiment was too explicit given the short timeframe. Fully addressing this possibility will require further data collection in future. The graph-specific effects that we observed indicated that meso-scale graph architecture impacts learning. Yet, these data do not address whether meso-scale architecture alone is privileged, or whether both smaller and larger scale topological features also play a role in the learning process. Using the random graph exposures, which had the greatest variability in multiscale network architecture, we studied (i) a measure of small scale topological structure in the node degree, which captures the extent to which a given node is connected to other nodes in a network, and (ii) a measure of large scale topological structure in the node betweenness centrality, which incorporates information about the role of a node within the entire graph by measuring its importance in shortest paths between other node pairs. We found that nodes of higher degree and betweenness centrality were associated with lower reaction times, as might be expected simply from the fact that learners would be more frequently exposed to these nodes via a random walk through the graph. Unexpectedly, however, after regressing out the number of times each node was visited, these relationships were inverted such that nodes of higher degree and betweenness centrality were associated with higher reaction times. This finding has important implications for how we understand the impact of smaller and larger scale architecture on learning. Specifically, at small topological scales, we propose that when learners are exposed to sequences generated by a heterogeneous topology such as is present in random graphs, a trade-off exists between repeated exposure to a given node (i.e., due to high degree) leading to a lower reaction time, and the complex representation introduced by the high number of its neighbors leading to a higher reaction time. At large topological scales, we similarly propose that a trade-off exists between repeated exposure to a given node due to its location along shortest paths in the graph, and the complexity of the possible paths along which it could be visited. In our current study, degree and betweenness centrality were significantly correlated with stimulus exposure. These nodes thus displayed a tradeoff between (i) familiarity, as modulated by the frequency of exposure, and (ii) uncertainty, as modulated by the probability of the future state being narrowly versus widely spread amongst motor actions. Understanding this potential tradeoff remains an important area for future work. One tractable strategy could be to consider other, non-random walks on the graph that would allow a node to exhibit both low familiarity and high uncertainty, or vice versa . It could also be useful to consider graph topologies that would allow variation in betweenness centrality without impacting degree, so as to disambiguate the effect of one versus the other. For example, graphs could be constructed in which low-degree nodes serve as bridges between disconnected areas of the graph, thus having high betweenness centrality. Separately modulating different local statistics on the graph, as well as the type of walk used, could allow for an expanded understanding of the topological drivers of the observed 17/29