PSYC 51.16 Computational Models of Behavior
Psychology and neuroscience have long sought to learn how brains function — such as, how we make decisions and learn from experience — by studying behavior during cognitive tasks. But how much can behavioral data really reveal about how the brain does what it does? This endeavor has been revolutionized by the development of computational models of behavior, mathematically defined algorithms describing mental processes that generate behavioral outputs from sensory and internal variables. In this approach, quantitative behavioral data can be compared to simulated behavior from models, and the model parameters can be fit to empirical data.
The goal of this course is to understand how computational models of behavior can be used to gain insight into psychological and neural processes. We will focus on canonical models of decision making and reinforcement learning. We will place emphasis on conceptual issues such as the purpose and logic of computational modeling and its role in experimental science.
Instructor
Murray