title: Beyond simple reinforcement learning: the computational neurobiology of reward-learning and valuation.
abstract: 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.
authors.forename: John P
authors.initials: JP
authors.lastname: O'Doherty
mesh_terms: D019295:Computational Biology / Q000379:methods* / Q000639:trends; D004298:Dopamine / Q000502:physiology; D006801:Humans; D007858:Learning / Q000502:physiology*; D009446:Neurobiology / Q000379:methods* / Q000639:trends; D012054:Reinforcement, Psychology; D012201:Reward; D013997:Time Factors
chemical_list: D004298:Dopamine