  So, for example, children might know that animals are arranged in a taxonomy of a specific sort, and this prior background knowledge helps them learn about animals. But how do they get this prior? It might be that they have a prior higher-order principle \(P\) that provides a probabilistic ordering on different graph structures, and that the taxonomy they use has a higher prior probability than other ways to structure the animal world (say, a ring structure). But how do they get \(P\)? Do they learn it or is it simply there innately? To tackle these questions, all sorts of objects—structured representations one finds in a grammar, graph-structures one might find in a taxonomic representation of causal or kin relations, schemas applied to scene or event analysis, etc.—will need to be formalized and assigned probabilities. So there is much to be done. There is no a priori answer about how far up the Bayesian can go, and we do well to keep an open mind about the nature of the unlearned priors. But we should also not overlook findings like the chick’s stubborn presumption of illumination from above, which suggest that nature can build in unlearned priors, and that they can be domain specific. It would be, at the very least, extremely surprising if nothing like this operates in human psychology.