Computational Models of Social Cognition

Dae Houlihan
COGS 50.09
Winter 2026


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 PPL memo.

Schedule

Date Topic Reading Assignments
Mon
Jan 5		 Introduction to the course
Wed
Jan 7		 Modeling cognition
• ontology and epistemology
• representation and abstraction
• levels of analysis
Mon
Jan 12		 What is a model? Probability as extended logic
Wed
Jan 14		 Belief
• recursive (level-k) beliefs
Fri
Jan 16		 PPL Exercise Due
Mon
Jan 19		 Martin Luther King Jr. Day
Class moved to Friday Jan 23
Wed
Jan 21		 Bayes is generative Tutorial: Bayes’ rule
Fri
Jan 23
5:30-7:20pm
NB Special Date & Time		 Causal models
Mon
Jan 26		 Modeling as philosophy
Wed
Jan 28		 Intuitive Theories
  • Gerstenberg & Tenenbaum (2017)
  • Lake et al. (2017)
  • Tutorial: Intuitive theories: what do we know?
Fri
Jan 30		 PPL Exercise Due

Learning Resources

Probabilistic Models of Cognition

Causal Modeling and Bayesian Data Analysis (BDA)

Probability Theory

memo

Acknowledgments

  • This course is heavily informed by ProbMods (Goodman et al., 2016).

    NoteCitation

    N. D. Goodman, J. B. Tenenbaum, and The ProbMods Contributors (2016). Probabilistic Models of Cognition (2nd ed.). Retrieved from http://probmods.org/

    BibTeX 
    @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}}
}

  • Huge thanks to Kartik Chandra for building memo, and adapting it for this course.

References

Chandra, Kartik, Chen, Tony, Tenenbaum, Joshua B., & Ragan-Kelley, Jonathan. (2025). A domain-specific probabilistic programming language for reasoning about reasoning (or: A memo on memo). Proc. ACM Program. Lang., 9. https://doi.org/10.1145/3763078
Cushman, Fiery. (2024). Computational Social Psychology. Annual Review of Psychology, 75(1), 625–652. https://doi.org/10.1146/annurev-psych-021323-040420
Gerstenberg, Tobias, & Tenenbaum, Joshua B. (2017). Intuitive Theories. In Michael Waldmannn (Ed.), Oxford handbook of causal reasoning (pp. 515–548). Oxford University Press.
Goodman, Noah D., Tenenbaum, Joshua B., & Contributors, The ProbMods. (2016). Probabilistic models of cognition (Second). http://probmods.org/v2
Griffiths, Thomas L., Chater, Nick, & Tenenbaum, Joshua B. (2024). Bayesian models of cognition: Reverse engineering the mind. The MIT Press.
Houlihan, Sean Dae, Kleiman-Weiner, Max, Hewitt, Luke B., Tenenbaum, Joshua B., & Saxe, Rebecca. (2023). Emotion prediction as computation over a generative theory of mind. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 381(2251), 20220047. https://doi.org/10.1098/rsta.2022.0047
Jaynes, Edwin T. (2003). Probability theory: The logic of science (G. L. Bretthorst, Ed.). Cambridge University Press. https://doi.org/10.1017/CBO9780511790423
Lake, Brenden M., Ullman, Tomer D., Tenenbaum, Joshua B., & Gershman, Samuel J. (2017). Building machines that learn and think like people. Behavioral and Brain Sciences, 40, e253. https://doi.org/10.1017/S0140525X16001837
Ma, Wei Ji, Kording, Konrad, & Goldreich, Daniel. (2023). Bayesian models of perception and action: An introduction. The MIT Press.
Marr, David. (1982). Vision: A computational investigation into the human representation and processing of visual information. W.H. Freeman.
McElreath, Richard. (2016). Statistical rethinking: A Bayesian course with examples in R and Stan. CRC Press/Taylor & Francis Group.
Moon, Katie, & Blackman, Deborah. (2014). A Guide to Understanding Social Science Research for Natural Scientists. Conservation Biology, 28(5), 1167–1177. https://doi.org/10.1111/cobi.12326
Poldrack, Russell A. (2021). The physics of representation. Synthese, 199(1), 1307–1325. https://doi.org/10.1007/s11229-020-02793-y
Shea, Nicholas. (2018). Representation in Cognitive Science (Vol. 1). Oxford University Press. https://doi.org/10.1093/oso/9780198812883.001.0001
Varela, Francisco J., Thompson, Evan, & Rosch, Eleanor. (1991). The embodied mind: Cognitive science and human experience. (pp. xx, 308). The MIT Press.