Preuschoff, Kerstin and Bossaerts, Peter (2007) Adding Prediction Risk to the Theory of Reward Learning. In: Reward and decision making in corticobasal ganglia networks. Annals of the New York Academy of Sciences (1104). New York Academy of Sciences , Boston, MA, pp. 135-146. ISBN 978-1-57331-674-3 http://resolver.caltech.edu/CaltechAUTHORS:20101014-101918850
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This article analyzesthe simple Rescorla–Wagner learning rule from the vantage point of least squares learning theory. In particular, it suggests how measures of risk, such as prediction risk, can be used to adjust the learning constant in reinforcement learning. It argues that prediction risk is most effectively incorporated by scaling the prediction errors. This way, the learning rate needs adjusting only when the covariance between optimal predictions and past (scaled) prediction errors changes. Evidence is discussed that suggests that the dopaminergic system in the (human and nonhuman) primate brain encodes prediction risk, and that prediction errors are indeed scaled with prediction risk (adaptive encoding).
|Item Type:||Book Section|
|Additional Information:||© 2007 New York Academy of Sciences. Article first published online: 20 Jun. 2007. We thank John O’Doherty for detailed comments on an earlier draft, and Tim Behrens, Mark Walton, and Matthew Rushworth for further discussions on the link between uncertainty and the learning rate. Peter Bossaerts thanks the Swiss Finance Institute for financial support during his stay at the Université de Lausanne, where this article was written.|
|Subject Keywords:||reinforcement learning; learning rate; least squares learning; dopaminergic system; reward anticipation; prediction risk; uncertainty; adaptive encoding|
|Official Citation:||PREUSCHOFF, K. and BOSSAERTS, P. (2007), Adding Prediction Risk to the Theory of Reward Learning. Annals of the New York Academy of Sciences, 1104: 135–146. doi: 10.1196/annals.1390.005|
|Usage Policy:||No commercial reproduction, distribution, display or performance rights in this work are provided.|
|Deposited By:||Jason Perez|
|Deposited On:||14 Oct 2010 18:33|
|Last Modified:||26 Dec 2012 12:32|
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