  We can use the case of language understanding, a well-studied area and arguably a Nativist stronghold, to illustrate how these Bayesian goals might be achieved. The phenomenon is familiar: you hear a sentence \(S_1\) as having a specific meaning. The theoretical approach mirrors the vision case: \(S_1\) (as auditorily processed) is compatible with a number of competing structural representations, but your parser somehow chooses the best one: \(\mathrm{sr}(S_1)'\). The Bayesian says that the parser is able to do this because it can do a statistical analysis that integrates bottom-up and top-down information. In this case, the bottom-up element is \(S_1\). But the range of possible structured representations the parser can select from is top-down information, as is the algorithm that chooses \(\mathrm{sr}(S_1)'\) over other candidates. The problem: how to assign a prior probability to a complex structured representation like \(\mathrm{sr}(S_1)'\) (for instance, a syntactic tree)—a probability that depends on the probability assigned to the sub-elements. We know how to assign the prior probability of a series of heads for a fair coin. But the question comes up again: how do we assign a prior probability to a linguistic representation, or to a complex visual scene, or to a complicated representation of the goals, roles, and perceptual beliefs of a player in one of the Theory of Mind scenarios? The events are more complex, the representations of the events are therefore more complex, and the hypothesis space is more complex (Chater et al. 2006).