Uncertainty-based competition between prefrontal and dorsolateral striatal systems for behavioral control.
A broad range of neural and behavioral data suggests that the brain contains multiple systems for behavioral choice, including one associated with prefrontal cortex and another with dorsolateral striatum. However, such a surfeit of control raises an additional choice problem: how to arbitrate between the systems when they disagree. Here, we consider dual-action choice systems from a normative perspective, using the computational theory of reinforcement learning. We identify a key trade-off pitting computational simplicity against the flexible and statistically efficient use of experience. The trade-off is realized in a competition between the dorsolateral striatal and prefrontal systems. We suggest a Bayesian principle of arbitration between them according to uncertainty, so each controller is deployed when it should be most accurate. This provides a unifying account of a wealth of experimental evidence about the factors favoring dominance by either system.
Daw
Nathaniel D
ND
Gatsby Computational Neuroscience Unit, University College London, Alexandra House, 17 Queen Square, London WC1N 3AR, UK. daw@gatsby.ucl.ac.uk
Niv
Yael
Y
Dayan
Peter
P
D000818:Animals; D001499:Bayes Theorem; D003071:Cognition / Q000502:physiology*; D003657:Decision Making / Q000502:physiology*; D006801:Humans; D008959:Models, Neurological; D017072:Neostriatum / Q000502:physiology*; D016571:Neural Networks, Computer; D009434:Neural Pathways / Q000502:physiology*; D017397:Prefrontal Cortex / Q000502:physiology*; D014836:Volition / Q000502:physiology*