Complex cognitive tasks present a range of computational and algorithmic challenges for neural accounts of both learning and inference. In particular, it is extremely hard to solve them using the sort of simple policies that have been extensively studied as solutions to elementary Markov decision problems. There has thus been recent interest in architectures for the instantiation and even learning of policies that are formally more complicated than these, involving operations such as gated working memory. However, the focus of these ideas and methods has largely been on what might best be considered as automatized, routine or, in the sense of animal conditioning, habitual, performance. Thus, they have yet to provide a route towards understanding the workings of rule-based control, which is critical for cognitively sophisticated competence. Here, we review a recent suggestion for a uniform architecture for habitual and rule-based execution, discuss some of the habitual mechanisms that underpin the use of rules, and consider a statistical relationship between rules and habits.

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