left: maps obtained from subsampled corpora, in which the encoded word appears in 16 and 128 documents, and from the full corpus. NeuroQuery needs less examples to learn a sensible brain map. NeuroSynth maps correspond to NeuroSynth’s Z scores for the ‘association test’ from neurosynth.org. NeuroSynth’s ‘posterior probability’ maps for these terms for the full corpus are shown in Figure 19. Each tool is trained on its own dataset, which is why the full-corpus occurrence counts differ. right: convergence of maps toward their value for the full corpus, as the number of occurrences increases. Averaged over 13 words: ‘language’, ‘auditory’, ‘emotional’, ‘hand’, ‘face’, ‘default mode’, ‘putamen’, ‘hippocampus’, ‘reward’, ‘spatial’, ‘amygdala’, ‘sentence’, ‘memory’. On average, NeuroQuery is closer to the full-corpus map. This confirms quantitatively what we observe for the two examples ‘language’ and ‘reward’ on the left. Note that here convergence is only measured with respect to the model’s own behavior on the full corpus, hence a high value does not indicate necessarily a good face validity of the maps with respect to neuroscience knowledge. The solid line represents the mean across the 13 words and the error bands represent a 95% confidence interval based on 1 000 bootstrap repetitions.