This Special Issue of EJN entitled “Beyond Simple Reinforcement Learning: the Computational Neurobiology of Reward-Learning and Valuation” (edited by John O’Doherty) was published online on April 4, 2012 (EJN issue 35-7).
Neural computational accounts of reward-learning have been dominated by the hypothesis that dopamine neurons behave like a reward-prediction error and thus facilitate reinforcement-learning in striatal target neurons. While this framework is consistent with a lot of behavioral and neural evidence, this theory fails to account for a number of behavioral and neurobiological observations. In this special issue of EJN we feature a combination of theoretical and experimental papers highlighting some of the explanatory challenges faced by simple reinforcement-learning models and describing some of the ways in which the framework is being extended in order to address these challenges. (From the Editorial written by John O’Doherty, guest editor of the Special Issue)