(A) Single neuron classification accuracy (mean ± s.e.m, n=177 neurons) per time bin, execution-trained classifiers tested with execution trials (e2e, blue), tested with observation trials (e2o, red). Observation-trained classifiers tested with observation trials (o2o, green), and execution trials (o2e, orange). Colored dots indicate time bins of the same color with significant accuracy above chance (Benjamini-Hochberg-corrected one-sided signed-rank tests, alpha <0.05). Triangles indicate time bins with significant differences in the accuracy between the indicated classifications (Benjamini-Hochberg-corrected two-sided signed-rank tests, alpha <0.05). For the abbreviations of the events, see Figure 2. The very low but above chance level accuracy of the e2e and o2o classifiers before LED onset indicates that the three conditions were already distinguishable to some extent before LED onset by cues we could not identify. (B) Population classification accuracy per time bin (mean and 90% CI derived from bootstrapping). Same as A, but here, all neurons (n=177) constructed the 177 features of a classifier. (C) Same as B, but the population consists of only 32 neurons with at least one bin with a shared code according to Figure 5A, right. (D) Same as B, but the population consists of only 17 neurons with only bins with a shared code according to a 10% threshold criterion and the boundary angle shown in Figure 4F. (E and F) Same as C and D, respectively, but for each time bin only neurons with a shared code in this bin were included, which leads to a variable population size (top), and in F, not only neurons with only bins with a shared code (as in D), but for each time bin all neurons with a shared code in this bin were included.