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.
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We are grateful to B. Balleine, A. Courville, A. Dickinson, P. Holland, D. Joel, S. McClure and M. Sahani for discussions. The authors are supported by the Gatsby Foundation, the EU Bayesian Inspired Brain and Artefacts (BIBA) project (P.D., N.D.), a Royal Society USA Research Fellowship (N.D.) and a Dan David Fellowship (Y.N.).
The authors declare no competing financial interests.
Value propagation in tree search, after 50 steps of learning the task in Figure 1a. (PDF 248 kb)
Example of learning in the cache algorithm, following a single transition from state s to s′ having taken action a. (PDF 306 kb)
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Daw, N., Niv, Y. & Dayan, P. Uncertainty-based competition between prefrontal and dorsolateral striatal systems for behavioral control. Nat Neurosci 8, 1704–1711 (2005). https://doi.org/10.1038/nn1560
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