The computational neurobiology of learning and reward.
Following the suggestion that midbrain dopaminergic neurons encode a signal, known as a 'reward prediction error', used by artificial intelligence algorithms for learning to choose advantageous actions, the study of the neural substrates for reward-based learning has been strongly influenced by computational theories. In recent work, such theories have been increasingly integrated into experimental design and analysis. Such hybrid approaches have offered detailed new insights into the function of a number of brain areas, especially the cortex and basal ganglia. In part this is because these approaches enable the study of neural correlates of subjective factors (such as a participant's beliefs about the reward to be received for performing some action) that the computational theories purport to quantify.
Gatsby Computational Neuroscience Unit, UCL, Alexandra House, 17 Queen Square, London, WC1N 3AR, UK. daw@gatsby.ucl.ac.uk
Nathaniel D
ND
Daw
Kenji
K
Doya
D000818:Animals; D001185:Artificial Intelligence; D001921:Brain / Q000033:anatomy & histology / Q000502:physiology*; D002540:Cerebral Cortex / Q000502:physiology; D003342:Corpus Striatum / Q000502:physiology; D003657:Decision Making / Q000502:physiology; D004298:Dopamine / Q000502:physiology; D006801:Humans; D007858:Learning / Q000502:physiology*; D016571:Neural Networks, Computer; D012201:Reward
D004298:Dopamine