Dialogues on prediction errors Export

@article{citeulike:2945405, abstract = {The recognition that computational ideas from reinforcement learning are relevant to the study of neural circuits has taken the cognitive neuroscience community by storm. A central tenet of these models is that discrepancies between actual and expected outcomes can be used for learning. Neural correlates of such prediction-error signals have been observed now in midbrain dopaminergic neurons, striatum, amygdala and even prefrontal cortex, and models incorporating prediction errors have been invoked to explain complex phenomena such as the transition from goal-directed to habitual behavior. Yet, like any revolution, the fast-paced progress has left an uneven understanding in its wake. Here, we provide answers to ten simple questions about prediction errors, with the aim of exposing both the strengths and the limitations of this active area of neuroscience research.}, author = {Niv, Yael and Schoenbaum, Geoffrey}, citeulike-article-id = {2945405}, citeulike-linkout-0 = {http://dx.doi.org/10.1016/j.tics.2008.03.006}, citeulike-linkout-1 = {http://linkinghub.elsevier.com/retrieve/pii/S136466130800137X}, citeulike-linkout-2 = {http://view.ncbi.nlm.nih.gov/pubmed/18567531}, citeulike-linkout-3 = {http://www.hubmed.org/display.cgi?uids=18567531}, citeulike-linkout-4 = {http://www.sciencedirect.com/science/article/B6VH9-4ST8XDX-1/1/8a6b6a90c7bbc7135364a820a91ffd0b}, doi = {10.1016/j.tics.2008.03.006}, journal = {Trends in Cognitive Sciences}, keywords = {dopamine, reinforcement-learning, review, td}, month = {July}, number = {7}, pages = {265--272}, posted-at = {2009-11-04 09:40:31}, priority = {2}, title = {Dialogues on prediction errors}, url = {http://dx.doi.org/10.1016/j.tics.2008.03.006}, volume = {12}, year = {2008} }

TY - JOUR ID - citeulike:2945405 L3 - citeulike-article-id:2945405 N2 - The recognition that computational ideas from reinforcement learning are relevant to the study of neural circuits has taken the cognitive neuroscience community by storm. A central tenet of these models is that discrepancies between actual and expected outcomes can be used for learning. Neural correlates of such prediction-error signals have been observed now in midbrain dopaminergic neurons, striatum, amygdala and even prefrontal cortex, and models incorporating prediction errors have been invoked to explain complex phenomena such as the transition from goal-directed to habitual behavior. Yet, like any revolution, the fast-paced progress has left an uneven understanding in its wake. Here, we provide answers to ten simple questions about prediction errors, with the aim of exposing both the strengths and the limitations of this active area of neuroscience research. IS - 7 JF - Trends in Cognitive Sciences EP - 272 TI - Dialogues on prediction errors VL - 12 SP - 265 KW - dopamine KW - reinforcement-learning KW - review KW - td AU - Niv, Yael AU - Schoenbaum, Geoffrey PY - 2008/07// UR - http://dx.doi.org/10.1016/j.tics.2008.03.006 ER -