  We do not yet know enough to settle these questions, but they are now beginning to be addressed. Most recently, a number of theorists have used Bayesian techniques to model not just low-level perceptual processing but also aspects of higher-order cognitive processes. Areas of current research include concept learning (Tenenbaum 1999), word learning (Xu 2007), and causal reasoning (Griffiths &amp; Tenenbaum 2005; Griffiths et al. 2011), and the list is  growing.[30] Contemporary research on the application of Bayesian techniques to higher-level cognition has generally ignored the battle lines of the Nativist-Empiricist debate. The real interest is in the possibility of developing statistical techniques that, as Tenenbaum et al 2006 puts it, “integrate bottom-up and top-down influences.” (For a very useful state of the art in Statistical learning along with concrete suggestions about deploying top-down information in such models, see Lake et al, forthcoming.)  We already have sophisticated statistical analyses of the bottom-up part; the perceptual phenomena. The challenge is to develop quantitative representations and analyses of the levels of top-down background knowledge that operate in particular domains. In section 2, for instance, we considered as part of the child’s background information his theory of mind. It was on the basis of this theory that the child could develop a structural analysis of a situation in terms of agents, beliefs, goals, help/hindrance, and so on. But the information contained in such a theory and the structural analyses of particular situations that this theory makes available, cannot yet be integrated into Bayesian statistical analyses. The challenge for Bayesians is to develop ways to recast the top-down elements and the analyses they make available in quantitative terms. Only then will we be in a position to address whether and to what extent top-down information is learned or innate.