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Computational Models of Human Social Behavior and Neuroscience

The goal of computational modeling in behavioral and psychological science is using mathematical models to characterize behavioral (or neural) data. Over the past decade, this practice has revolutionized social psychological science (and neuroscience) by allowing researchers to formalize theories as constrained mathematical models and test specific hypotheses to explain unobservable aspects of complex social cognitive processes and behaviors. This course is composed of 4 modules in the format of Jupyter Notebooks. This course comprises lecture-based, discussion-based, and lab-based instruction. At least one-third of class sessions will be hands-on. We will discuss relevant book chapters and journal articles, and work with simulated and real data using the Python programming language (no prior programming experience necessary) as we survey some selected areas of research at the intersection of computational modeling and social behavior. These selected topics will span a broad set of social psychological abilities including (1) learning from and for others, (2) learning about others, and (3) social influence on decision-making and mental states. Rhoads, S. A. & Gan, L. (2022). Computational models of human social behavior and neuroscience - An open educational course and Jupyter Book to advance computational training.  ​​​Journal of Open Source Education5(47), 146.


Topics covered in this lesson
  • Module 01
    • Jupyter Noteboks
    • Introduction to Python basics
    • Working with data
    • Python exercises
  • Module 02
    • Linear Modeling
    • Non-linear Modeling
    • Modeling exercises
  • Module 03
    • Two-armed Bandit
    • Models of learning
    • RL exercises
  • Module 04
    • Social learning
    • Prosocial RL exercises
    • Future directions
  • Research methods and statistics
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