Simple substrates for complex cognition

Peter Dayan
2008 Frontiers in Neuroscience  
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
more » ... ng 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. Peter Dayan is a Professor of Computational Neuroscience at UCL built mathematical and computational models of neural processing, with a particular emphasis on representation and learning. The main focus is on reinforcement learning and unsupervised learning, covering the ways that animals come to choose appropriate actions in the face of rewards and punishments, and the ways and goals of the process by which they come to form neural representations of the world. The models are informed and constrained by neurobiological, psychological and ethological data. Frontiers in Neuroscience December 2008 | Volume 2 | Issue 2 | 256 Dayan Simple substrates for complex cognition
doi:10.3389/neuro.01.031.2008 pmid:19225599 pmcid:PMC2622746 fatcat:wgxr3rzvkfdfrglt4imby6mdn4