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. Static Branch Prediction. Code around delayed branch. To reorder code around branches, need to predict branch statically when compile . Simplest scheme is to predict a branch as taken. Average misprediction = untaken branch frequency = 34% SPEC. Reinforcement learning I: . prediction . classical conditioning . dopamine. Reinforcement learning II:. dynamic programming; action selection. Pavlovian. . misbehaviour. vigor. Chapter 9 of Theoretical Neuroscience. 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.. CS 3220. Fall 2014. Hadi Esmaeilzadeh. hadi@cc.gatech.edu. . Georgia Institute of Technology. Some slides adopted from Prof. . Milos . Prvulovic. Control Hazards Revisited. Forwarding helps a lot with data hazards. Winston P. Nagan . With the assistance of Megan E. Weeren . April 10, 2015. Anticipation will invariably entail complexity in the context of the individual self systems functioning in the social process and interacting in social relations.. 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. Emura. , Chen & Chen [ 2012, . PLoS. ONE 7(10) ] . Takeshi . Emura. (NCU). Joint work with Dr. Yi-. Hau. Chen and Dr. . Hsuan. -Yu Chen (. Sinica. ). 國立東華大學 應用數學系. 1. 2013/5/17. 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. Matthew S. Gerber, Ph.D.. Assistant Professor. Department of Systems and Information Engineering. University of Virginia. IACA Presentations on Social Media. The Modern Analyst. and Social Media (Woodward). 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. 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.