Nick Chater has put forward a bold claim in his recent book, The Mind Is Flat, as well as in an article and interview in Nautilus: that we don't have any unconscious thoughts. A metaphor that Chater, a behavioral scientist, dislikes is that of the iceberg, the tip of which is our consciousness, and the vast, submerged part is our unconscious. As Chater says in the Nautilus interview, this suggests that unconscious and conscious processes use the same kinds of representations, and that the kinds of things we are unconscious of we could be conscious of. He's certainly right that many brain processes go on that we're unaware of, and can't be aware of. Let's take visual recognition as an example.

A major target of linguistics and cognitive science has been to understand what class of learning systems can acquire the key structures of natural language. Until recently, the computational requirements of language have been used to argue that learning is impossible without a highly constrained hypothesis space. Here, we describe a learning system that is maximally unconstrained, operating over the space of all computations, and is able to acquire several of the key structures present natural language from positive evidence alone. The model successfully acquires regular (e.g. $(ab)^n$), context-free (e.g. $a^n b^n$, $x x^R$), and context-sensitive (e.g. $a^nb^nc^n$, $a^nb^mc^nd^m$, $xx$) formal languages. Our approach develops the concept of factorized programs in Bayesian program induction in order to help manage the complexity of representation. We show in learning, the model predicts several phenomena empirically observed in human grammar acquisition experiments.