experiment. Mixed effects models were fit using the lme4 library in R (R v3.5.0, lme4 v1.1-17), using the lmer function. Predictors were centered to reduce multicollinearity. All contrasts were orthogonally coded. The observed correlation between fixed effects was less than 0.7. Random effects were chosen as the maximal structure that allowed model convergence, as specified in the next section. All tests were two-sided unless otherwise noted. For every trial, we computed the reaction time based on the elapsed time from the last button press. We only excluded trials for two reasons: subjects answered incorrectly on their first attempt or the reaction time was implausible (under 100ms or over 5000ms, or more than 3 SDs from their mean reaction time). Effect of Targets on Reaction Time: Since performance was measured based on a key press, it was important to determine whether biomechanical factors related to the use of different fingers, or to different combinations of finger pairs, influenced reaction time. We predicted that one-finger responses would show shorter reaction times than two-finger responses, and that response time would vary based on the finger needed in the response. We calculated the average reaction time for each key press or combination of key presses across subjects and training stages (Fig. 1C). While we observed complex differences in reaction time by finger, we also found robust differences in reaction time driven by the number of fingers required, with one-key presses displaying a shorter reaction time than two-key presses (paired two-tailed t -test for one- and two-finger means for each subject: t ( 321 ) = 35 . 56 , p < 0 . 001; mean difference: 228.86 ms, 95% confidence interval: 216.20 to 241.52 ms). We observed that the relative ordering of keys or key combinations by reaction time was remarkably well-preserved across subjects, and the difference was independent of which graph was seen in the first stage of the experiment (random, lattice, or modular). This observation suggests unique motor timing associated with each target (Supplementary Fig. 8). To ensure that our findings were not systematically biased by differences in reaction time across key presses, we included the target key press as a regressor in all statistical models. Learning Rate: Based on a subject’s reaction time profile across the session, we estimated each subject’s learning rate from a linear mixed effects model. The learning rate estimate was the per-subject random effect for trial number. A faster learning rate was captured as a negative coefficient, which indicated that a subject’s reaction time decreased more rapidly over time. The model was fit to the first stage of the first experiment, with formula RT ∼ log ( Trial ) ∗ Graph + Target +( 1 + log ( Trial ) | Subject ) , where Target represented the key combination of the target node, controlling for biomechanical differences in motor response, 23/29 Analytical Approach