• ☰
  • Home
  • Introduction
  • Generative models
  • Conditioning
  • Dependence
  • Conditional dependence
  • Social cognition

This course introduces the philosophy and practice of building computational models of social cognition. The course will follow three themes. The topic-theme, social cognition, will explore how people infer beliefs and preferences, plan interactions, and reason about emotions. Other topics in cognitive science will be incorporated to build a conceptual foundation for thinking about how people represent each other. The theoretical-theme, models as epistemological expressions, will emphasize thinking deeply about why and how models are made and used. We will consider how scientists’ views on the mind and the world shape their approaches to building models of the mind, models of people’s mental models of the world, and models of people’s mental models of other minds. We will examine aspects of modeling that are often implicit or deemphasized, including what alternative models were not chosen, the philosophical traditions behind modeling assumptions, how information is learned and represented by models, how data is measured and used, and the various goals and functions of modeling. The methodological-theme, probabilistic programming, will give you a skillset for building formal cognitive models. You will be introduced to probabilistic programming using the WebPPL language.

Links

Canvas (readings, lectures, etc.)

Ed Discussion (forum)

Schedule

Sections

1. Introduction
   A brief introduction to the philosophy.
2. Generative models
   Representing working models with probabilistic programs.
3. Conditioning
   Asking questions of models by conditional inference.
4. Dependence
   Causal and statistical dependence.
5. Conditional dependence
   Patterns of inference as evidence changes.
6. Social cognition
   Inference about inference

Acknowledgments

This course website is a port of the open source project, ProbMods.

@misc{probmods2,
  title = {{Probabilistic Models of Cognition}},
  edition = {Second},
  author = {Goodman, Noah D and Tenenbaum, Joshua B. and The ProbMods Contributors},
  year = {2016},
  howpublished = {\url{http://probmods.org/v2}},
  note = {Accessed: }
}