Before revision:  From this perspective, a translating principle between cognitive science and machine learning can be obtained that should result in productive cross-fertilization between these disciplines: simply, cortical columns represent nodes in a particular kind of directed graphical model, where the symbolic level emerges from the sub-symbolic level through hierarchical pattern abstraction.
After revision:  From the analogical structure mapping perspective, thought is constituted by the alignment and comparison of heterogeneous structural representations. Here I will propose that this core cognitive process for analogical reasoning is naturally implemented by predictive coding mechanisms. However, both Bayesian cognitive science and models of cognitive development via analogical reasoning require rich base domains and priors (or reliably learnable posteriors) from which they can commence the process of bootstrapping minds. Here in the sub-symbolic level through hierarchical pattern abstraction.