Adding Prediction Risk to the Theory of Reward Learning Export

@article{citeulike:1421145, abstract = {This article analyzesthe simple Rescorla2013Wagner 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).}, address = {Computation and Neural Systems, California Institute of Technology, Pasadena, California, USA}, author = {Kerstin Preuschof, F. and Peter Bossaert, S.}, citeulike-article-id = {1421145}, citeulike-linkout-0 = {http://dx.doi.org/10.1196/annals.1390.005}, citeulike-linkout-1 = {http://www.ingentaconnect.com/content/bsc/nyas/2007/00001104/00000001/art00010}, citeulike-linkout-2 = {http://www3.interscience.wiley.com/cgi-bin/abstract/117985561/ABSTRACT}, doi = {10.1196/annals.1390.005}, issn = {1749-6632}, journal = {Annals of the New York Academy of Sciences}, keywords = {adaptive\_learning\_rate, dopamine, learning, reinforcement\_learning, rescorla-wagner, uncertainty}, month = {May}, number = {Reward and Decision Making in Corticobasal Ganglia Networks}, pages = {135--146}, posted-at = {2009-11-04 15:57:35}, priority = {2}, publisher = {Blackwell Publishing}, title = {Adding Prediction Risk to the Theory of Reward Learning}, url = {http://dx.doi.org/10.1196/annals.1390.005}, volume = {1104}, year = {2007} }

TY - JOUR ID - citeulike:1421145 L3 - citeulike-article-id:1421145 N2 - This article analyzesthe simple Rescorla2013Wagner 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). IS - Reward and Decision Making in Corticobasal Ganglia Networks JF - Annals of the New York Academy of Sciences SN - 1749-6632 AD - Computation and Neural Systems, California Institute of Technology, Pasadena, California, USA EP - 146 TI - Adding Prediction Risk to the Theory of Reward Learning PB - Blackwell Publishing VL - 1104 SP - 135 KW - adaptive_learning_rate KW - dopamine KW - learning KW - reinforcement_learning KW - rescorla-wagner KW - uncertainty AU - Kerstin Preuschof, F AU - Peter Bossaert, S PY - 2007/05// UR - http://dx.doi.org/10.1196/annals.1390.005 ER -