Abstract
AbstractThe dominant theoretical framework to account for reinforcement learning in the brain is temporal difference learning (TD) learning, whereby certain units signal reward prediction errors (RPE). The TD algorithm has been traditionally mapped onto the dopaminergic system, as firing properties of dopamine neurons can resemble RPEs. However, certain predictions of TD learning are inconsistent with experimental results, and previous implementations of the algorithm have made unscalable assumptions regarding stimulus-specific fixed temporal bases. We propose an alternate framework to describe dopamine signaling in the brain, FLEX (Flexibly Learned Errors in Expected Reward). In FLEX, dopamine release is similar, but not identical to RPE, leading to predictions that contrast to those of TD. While FLEX itself is a general theoretical framework, we describe a specific, biophysically plausible implementation, the results of which are consistent with a preponderance of both existing and reanalyzed experimental data.
Funder
U.S. Department of Health & Human Services | NIH | National Institute of Biomedical Imaging and Bioengineering
United States Department of Defense | United States Navy | ONR | Office of Naval Research Global
Simons Foundation
RCUK | Biotechnology and Biological Sciences Research Council
Wellcome Trust
Publisher
Springer Science and Business Media LLC
Reference78 articles.
1. Sutton, R. S. & Barto, A. G. Reinforcement Learning, Second Edition: An Introduction. (MIT Press, 2018).
2. Glickman, S. E. & Schiff, B. B. A biological theory of reinforcement. Psychol. Rev. 74, 81–109 (1967).
3. Lee, D., Seo, H. & Jung, M. W. Neural basis of reinforcement learning and decision making. Annu. Rev. Neurosci. 35, 287–308 (2012).
4. Chersi, F. & Burgess, N. The cognitive architecture of spatial navigation: hippocampal and striatal contributions. Neuron 88, 64–77 (2015).
5. Schultz, W., Dayan, P. & Montague, P. R. A neural substrate of prediction and reward. Science 275, 1593–1599 (1997).