Supplementary Figure 3: Additional results for gain patterns providing motor primitives.

a, The resulting distribution of gains from training independently on each of 100 target outputs (see our simulation details). The distribution of the gain patterns resembles a normal distribution (blue curve) with the same mean and variance as those in Fig. 1e. b, Each output from the 100 trained gain patterns. c, Outputs of 100 randomly-generated gain patterns from the distribution in a. (See our simulation details and our full simulation descriptions in the supplementary material for further details.) The outputs are substantially more homogeneous than those in b and likely would not constitute a good library for movement generation. d, The same plot as in Fig. 4d, but for up to l = 50 library elements. e, The distributions of errors across 100 different libraries for (left) l = 5 and (right) l = 20. (Note the difference in horizontal-axis scales in the two plots.) f, The error between the output and the fit from d with a different vertical axis scale. g, The same plot as in Fig. 4c, but for l = 1,..., 50 and with extended axes. Each point represents the 50th-smallest error between the output and the fit across 100 novel target movements for each of 100 randomly-generated combinations of l library elements. We show the identity line in gray. h, The same as in g, but each point represents the 50th-smallest error between the output and the fit across the 100 libraries for each of the 100 novel target movements. We plot these data in the square [0, 1] × [0, 1] and for l = 1,..., 20. i, For the data in g, we plot the Pearson correlation coefficient between the output and the fit errors over the 100 randomly-generated libraries for each number of library elements (up to l = 50). j, For the data in h, we plot the Pearson correlation coefficient between the output and the fit errors over the 100 novel target movements for each number of library elements (up to l  = 50)