Dopamine Neurons Respond to Errors in the Prediction of Sensory Features of Expected Rewards.
Midbrain dopamine neurons have been proposed to signal prediction errors as defined in model-free reinforcement learning algorithms. While these algorithms have been extremely powerful in interpreting dopamine activity, these models do not register any error unless there is a difference between the value of what is predicted and what is received. Yet learning often occurs in response to changes in the unique features that characterize what is received, sometimes with no change in its value at all. Here, we show that classic error-signaling dopamine neurons also respond to changes in value-neutral sensory features of an expected reward. This suggests that dopamine neurons have access to a wider variety of information than contemplated by the models currently used to interpret their activity and that, while their firing may conform to predictions of these models in some cases, they are not restricted to signaling errors in the prediction of value.
['Takahashi|Yuji K|YK|', 'Batchelor|Hannah M|HM|', 'Liu|Bing|B|', 'Khanna|Akash|A|', 'Morales|Marisela|M|', 'Schoenbaum|Geoffrey|G|']
D000818:Animals D030801:Animals, Genetically Modified D003216:Conditioning, Operant D059290:Dopaminergic Neurons D008297:Male D008959:Models, Neurological D051381:Rats D012201:Reward D012677:Sensation D017557:Ventral Tegmental Area
dopamine prediction error learning rodent single unit