title: Learning Reward Uncertainty in the Basal Ganglia.
abstract: Learning the reliability of different sources of rewards is critical for making optimal choices. However, despite the existence of detailed theory describing how the expected reward is learned in the basal ganglia, it is not known how reward uncertainty is estimated in these circuits. This paper presents a class of models that encode both the mean reward and the spread of the rewards, the former in the difference between the synaptic weights of D1 and D2 neurons, and the latter in their sum. In the models, the tendency to seek (or avoid) options with variable reward can be controlled by increasing (or decreasing) the tonic level of dopamine. The models are consistent with the physiology of and synaptic plasticity in the basal ganglia, they explain the effects of dopaminergic manipulations on choices involving risks, and they make multiple experimental predictions.
authors: Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom.|Harvard Medical School, Boston, Massachusetts, United States of America.
authors: John G
authors: 0000-0002-7622-716X
authors: JG
authors: Mikhael
authors: MRC Brain Network Dynamics Unit, University of Oxford, Oxford, United Kingdom.|Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom.
authors: Rafal
authors: R
authors: Bogacz
mesh_terms: D001479:Basal Ganglia / Q000502:physiology*; D002755:Choice Behavior / Q000502:physiology*; D019295:Computational Biology; D006801:Humans; D007858:Learning / Q000502:physiology*; D008959:Models, Neurological; D012201:Reward; D035501:Uncertainty