  Bayesian approaches are appealing because they provide a natural way to solve the problem that troubles theories, like Connectionism, that are built on associationist lines. Associationist learning is bottom-up. It depends on keeping track of correlations in the stream of experience and slowly modulating expectations on the basis of these correlations. But as we noted earlier, humans and animals learn about the world very quickly, and on the basis of a very small number of exposures and interventions. A child hears the word ‘horse’ applied to a few instances (and probably hears stray utterances of the word too) and reliably learns the extension of the term (Markman 1989). A rat made sick by a food one time, will not eat food with that smell again (Garcia et al 1955). These ‘fast-mappings’ are a problem for Associationist models. But they are more easily accommodated in Bayesian models, which essentially quantify the role of background knowledge—the top-down contribution—in the fixation of belief. If the rat already knows, as part of its background knowledge—its ‘factory settings’, so to speak—that when it comes to foods, smell is an indicator of edibility, then single-case learning is less mysterious. The prior probability of hypotheses linking edibility to smell may be antecedently set as very high, and hypotheses linking edibility to orientation may be set as very low. So one association between sick-making food \(f\) and smell \(s\) will be enough for the rat to ‘adopt the hypothesis’ that \(f\) and \(s\) are regularly linked. In contrast, if sick-making food \(f\) is always in a particular orientation \(o\), the rat may have a hard time making the connection even if it may be sensitive to orientations in other contexts. Similarly, if the child comes to the word-learning task with the assumption that new words most likely pick out unfamiliar extensions—again, with this assumption implemented in the priors—then her job is made easier. Bayes’ Theorem gives us a way to factor in this top-down background  knowledge.[28]