As with many clustering algorithms, PrAGMATiC optimizes a non-convex objective function and so can find many potential locally optimal solutions. To reduce the effect of non-convexity on our results, we re-fit the model ten times (each time with a different random initialization), and then selected the model fit that yielded the best likelihood (that is, performance on the training set) as the PrAGMATiC atlas (Fig. 3). Here we show the PrAGMATiC atlas (top) and the second best model out of the ten that were estimated (bottom). The parcellations given by these two models are very similar. However, there are a few differences, which illustrate uncertainty in the model. Some of these differences are due to statistical thresholding: a few areas that were found to be significantly semantically selective in the best model are missing in the alternative model (see left medial prefrontal cortex), and some significant areas in the alternate model are missing from the best model (left ventral occipital cortex). Other differences suggest alternative parcellations for a few regions, where, for example, the same region of cortex is parcellated into three areas in the best model and four areas in the alternative model. Yet it is clear that none of the differences between these two models are sufficient to change any of the interpretations given in the main text.