PPT-1 dopamine and prediction error

no prediction prediction reward prediction no reward TD error V t R R L Schultz 1997 humans are no different dorsomedial striatumPFC goaldirected control dorsolateral striatum habitual control. The idea is that rather than a plain least squares approach or a statistical maximum likelihood approach there is a third important principle in use for estimating the parameters of a dynamic model based on recorded observations This technique consi Interest in dopamine was intensified by the realisation that dopamine had an important role in the pathogenesis or drug treatment of certain brain History diseases eg Parkinsons disease schizophrenia This lead to much research on the sites of action Reinforcement learning I: . prediction . classical conditioning . dopamine. Reinforcement learning II:. dynamic programming; action selection. Pavlovian. . misbehaviour. vigor. Chapter 9 of Theoretical Neuroscience. no prediction. prediction, reward. prediction, no reward. TD error. V. t. R. R. L. Schultz 1997. humans are no different. dorsomedial. striatum/PFC. goal-directed control. dorsolateral. striatum. habitual control. Assumptions on noise in linear regression allow us to estimate the prediction variance due to the noise at any point.. Prediction variance is usually large when you are far from a data point.. We distinguish between interpolation, when we are in the convex hull of the data points, and extrapolation where we are outside.. . Each type of feature reduces the error rate over the baseline.. SRF and INF features appear to be more predictive than SIF features.. Overall reduction can be as large as 32. % over . the baseline error when all features are combined.. Fundamental Prediction Error: Self-Others Discrepancies in Risk Preference K. Hsee University of Chicago Elke U. Weber Ohio State University This research examined whether people can accurately pre Prediction is important for action selection. The problem:. prediction of future reward. The algorithm:. temporal difference learning. Neural implementation:. dopamine dependent learning in BG. A precise computational model of learning allows one to look in the brain for “hidden variables” postulated by the model. Prediction is important for action selection. The problem:. prediction of future reward. The algorithm:. temporal difference learning. Neural implementation:. dopamine dependent learning in BG. A precise computational model of learning allows one to look in the brain for “hidden variables” postulated by the model. Reinforcement learning I: . prediction . classical conditioning . dopamine. Reinforcement learning II:. dynamic programming; action selection. Pavlovian. . misbehaviour. vigor. Chapter 9 of Theoretical Neuroscience. Oswaldo. Carrillo. Ruth Yanai. The State University of New York. Visit our website: . www.quantifyinguncertainty.org. Download papers and presentations. Share sample code. Stay updated with QUEST News. prediction . classical conditioning . dopamine. Reinforcement learning II:. dynamic programming; action selection. Pavlovian. . misbehaviour. vigor. Chapter 9 of Theoretical Neuroscience. (thanks to Yael . Wayne . Wakeland. Systems . Science . Seminar . Presenation. 10/9/15. 1. Assertion. Models . must, of course, be . well suited to their intended . application. Thus, . models . for evaluating . policies must be able to . Cognitive Neuroscience. David Eagleman . Jonathan . Downar. Chapter Outline. Motivation and Survival. The Circuitry of Motivation: Basic Drives. Reward, Learning, and the Brain. Opioids and the Sensation of Pleasure.