The computational neurobiology of learning and reward

@article{citeulike:1237233, abstract = {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.}, author = {Daw, Nathaniel D. and Doya, Kenji }, booktitle = {Cognitive neuroscience}, citeulike-article-id = {1237233}, doi = {10.1016/j.conb.2006.03.006}, journal = {Current Opinion in Neurobiology}, keywords = {learning, model, reinforcement-learning, reward}, month = {April}, number = {2}, pages = {199--204}, posted-at = {2007-07-09 12:47:24}, priority = {0}, title = {The computational neurobiology of learning and reward}, url = {http://dx.doi.org/10.1016/j.conb.2006.03.006}, volume = {16}, year = {2006} }

TY - JOUR ID - citeulike:1237233 L3 - citeulike-article-id:1237233 TI - The computational neurobiology of learning and reward T2 - Cognitive neuroscience JF - Current Opinion in Neurobiology VL - 16 IS - 2 SP - 199 EP - 204 N2 - 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. KW - learning KW - model KW - reinforcement-learning KW - reward AU - Daw, Nathaniel AU - Doya, Kenji PY - 2006/04// UR - http://dx.doi.org/10.1016/j.conb.2006.03.006 ER -