PPT-Dopamine enhances model-based over model-free choice

behavior Peter Smittenaar Klaus Wunderlich Ray Dolan Modelbased and modelfree systems modelfree habitual Cached values single stored value Learned over many repetitions TD prediction error. William Greene. Stern School of Business. New York University. Part 7-1. Latent Class Models. Discrete Parameter Heterogeneity. Latent Classes. Latent Class Probabilities. Ambiguous – Classical Bayesian model?. William Greene. Stern School of Business. New York University. Part 11. Modeling Heterogeneity. Several Types of Heterogeneity. Observational: Observable differences across. choice makers. Choice strategy: How consumers make. 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. 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. 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. Tolplan (PTY) Ltd. APPLICATION OF STATED PREFERENCE CHOICE MODELS IN THE CITY OF JOHANNESBURG TRANSPORT DEMAND MODEL. Content:. Background. COJ Transport demand model design. SP surveys. Mode choice design . the Tampa, Florida Metropolitan Region. Monique Stinson, Zahra Pourabdollahi, Richard Tillery, Kai Zuehlke. May 2015. Acknowledgment. Florida Department of Transportation – District 7. Overview. Introduction. Erik Sabina. Jennifer Malm. Suzanne Childress. John Bowman. DeVon Culbertson. Erik’s recommendations . (and the price you’ll have to pay). Get into the critical path. Though it will put the hurt on schedule. TRB Transportation Planning Applications 2011 . |. Reno, NV. Rick Donnelly & Tara Weidner. | . PB . |. [. donnellyr. , . weidner. ]@. pbworld.com. Overview. Concepts. Albuquerque HBW example (urban). Discrete Choice Modeling William Greene Stern School of Business New York University Part 2 Estimating and Using Binary Choice Models Agenda A Basic Model for Binary Choice Specification Maximum Likelihood Estimation CS786. 31. st. March 2022. Cognitive maps in rats and men. Revisiting an old debate. Model free learning .  learn stimulus-response mappings = habits. What about goal-based decision-making?. Do animals not learn the physics of the world in making decisions?. Goals. What is an agent-based model. ?. Why . construct agent-based models. ?. What are the parts of an agent-based model?. How does one construct agent-based models?. What is an agent-based model?. What is an agent-based model?. 9 december 2019. Guest Lecture by Luuk Brederode. For: course . CIE5802-09 | Advanced transportation modelling, Delft University. Contents. Introduction: travel demand models. Why tour- or activity-based? . Cihan. Kaya. 12/15/2015. Aims. Build and simulate a spatial model of dopaminergic signaling.. Investigate the effects of . Spatial distribution or surface density fluctuations of dopamine transporters.