RT variation. The separability of perceptual and motor learning in this and similar tasks continues to be a matter of debate. For example, Deroost & Soetens 39 found that, in the context of an SRT task, stimulus-stimulus (i.e., perceptual) learning was limited to simple deterministic sequences. After training participants on more complex probabilistic sequences, they did not find evidence of perceptual learning. The separability of perceptual and motor learning systems is a challenging issue and one that is not yet fully resolved (e.g., it is likely that one system bolsters the other). Unfortunately, because the present set of experiments was not designed to address this issue, we cannot make strong claims one way or the other. Thus, we elect to maintain the most conservative interpretation of the data possible: our task involves sequences of motor responses, so we frame our results as evidence of motor skill learning. However, teasing apart perceptual and motor learning under this framework is a fascinating area for future study. Especially when considering closely related prior work$^{3,4}$, we suggest that our observed pattern of results is likely reflective of domain-general processes. We note a few methodological considerations that are particularly pertinent to this work. First, there exists a broad literature on the theory of graph structure as well as on structures found in natural stimuli. Here we sample only a small portion of possible graphs representing stimulus relationships. We study two stereotyped graphs, one with meso-scale clustering (modular) and one with no meso-scale clustering (lattice). However, graphs can exhibit other diverse topologies such as core-periphery structure, as well as other configurations of both high and low clustering beyond those tested. Moreover, the graphs we examine are all comprised of 15 nodes. While impractical for the current study, the relationships between natural stimuli might best be represented by graphs composed of hundreds or even thousands of nodes. Thus an open question is how these results generalize to both larger and more diverse networks. Second, we collected no personal information on Mechanical Turk participants. While we screened for eligibility using location and browser, we collected no information on handedness, age, or prior typing experience. However, as our regression models all incorporate per-subject baseline and learning rate effects, we expect minimal impact on our results from any between-subject differences. Third, there exist several limitations to using participants from Mechanical Turk, who might each be viewing the experiment on different browsers and with different levels of accuracy and speed in their internet connection. Fourth, it is possible that reaction time differences might be driven by recency priming. We know that processing times are reduced for a stimulus recently viewed by the learner, and that different nodes within a graph may be differentially affected. This is primarily a concern for modeling the cross-cluster surprisal effect, where transition nodes may have not been seen as recently as pre-transition nodes. However, Karuza et al. 4 found that low level perceptual priming effects did not fully account for the observed cross-cluster surprisal effect. Moreover, all between-graph comparisons are 18/29