Computational Models of Social Cognition

Dae Houlihan

COGS 50.09

Winter 2025

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.

Course Links

Syllabus
Canvas (readings, lectures, etc.)

Schedule

Date	Topic	Reading	Assignments
Mon Jan 6	Introduction to the course		Read the Syllabus
Wed Jan 8	Modeling as philosophy • ontology and epistimology • representation and abstraction • levels of analysis	Marr (1982, Ch. 1 (1.1–1.2)) Varela, Thompson, and Rosch (1991, Ch. 1) McElreath (2016, Ch. 1) Poldrack (2021) Moon and Blackman (2014)	Due: Reading responses Due: Setup python env Tutorial: Generative Models 1
Mon Jan 13	Belief • intentional stance • recursive (level-k) beliefs • false belief	Jaynes (2003, Preface and Ch. 1)	Tutorial: 2/3rds Game
Wed Jan 15	Conditioning generative models • Bayes’ rule • cultures and traditions of modeling • types of uncertainty	Tenenbaum et al. (2011) Betancourt (2021) Cusimano, Hewitt, and McDermott (2024) Betancourt (2019)
Fri Jan 17	*Coding Helproom* Time: 3–5 PM Location: Moore Hall - CIM Space (Room 213-217)
Mon Jan 20	Martin Luther King Jr. Day Class moved to Tuesday Jan 21
Tue Jan 21	NB Special date, time and location: Time: 4:30-6:20 PM Location: Fairchild 008	Gerstenberg and Tenenbaum (2017) Goodman et al. (2006)
Tue Jan 21	PPL Exercise Due		Due: Generative Models 1

Learning Resources

Probabilistic Models of Cognition

ProbMods by Noah Goodman, Joshua Tenenbaum, and The ProbMods Contributors
- An excellent introduction to probabilistic models of cognition, and probabilistic programming more generally, focusing on interactive exercises with WebPPL.
- Also see:
  - The Design and Implementation of Probabilistic Programming Languages by Noah Goodman and Andreas Stuhlmüller
  - Modeling Agents with Probabilistic Programs by Owain Evans, Andreas Stuhlmüller, John Salvatier, and Daniel Filan
  - Probabilistic language understanding: An introduction to the Rational Speech Act framework by Gregory Scontras, Michael Henry Tessler, and Michael Franke
Bayesian Cognitive Modeling by Michael D. Lee and Eric-Jan Wagenmakers
Bayesian Models of Perception and Action by Wei Ji Ma, Konrad Kording, and Daniel Goldreich

Causal Modeling and Bayesian Data Analysis (BDA)

Statistical Rethinking by Richard McElreath
- An excellent and accessible introduction to causal modeling.
- In addition to the textbook, see the lectures and programming exercises in a variety of PPL.
Bayesian Data Analysis by Andrew Gelman, John Carlin, Hal Stern, David Dunson, Aki Vehtari, and Donald Rubin
- Full text available from the authors.
Doing Bayesian Data Analysis by John K. Kruschke
Probabilistic Machine Learning: An Introduction by Kevin Patrick Murphy
- Full text available from the author.
Causality: Models, Reasoning, and Inference by Judea Pearl
- Full text available from the publisher.

Probability Theory

Probability by The Khan Academy.
- A gentle introduction to probability.
Probability Bootcamp by Steve Brunton
- Lectures covering the foundations of probability theory.
Probabilistic Models by Michael Betancourt
- A work-in-progress textbook that goes into the mathematical foundations underlying causal models. A great theoretical grounding of probabilistic modeling and introduction to measure theory.
Probability Theory: The Logic of Science by Edwin Thompson Jaynes
- Full text available from the publisher.
Introduction to Probability by Dimitri P. Bertsekas and John N. Tsitsiklis

`memo`

Acknowledgments

This course is heavily informed by ProbMods (Goodman, Tenenbaum, and Contributors 2016).
Citation
N. D. Goodman, J. B. Tenenbaum, and The ProbMods Contributors (2016). Probabilistic Models of Cognition (2nd ed.). Retrieved from https://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 and Tony Chen building memo, and to Kartik for adapting memo for this course.

References

Chandra, Kartik, Tony Chen, Joshua Tenenbaum, and Jonathan Ragan-Kelley. 2025. “A Domain-Specific Probabilistic Programming Language for Reasoning About Reasoning (or: A Memo on Memo).” January 9, 2025. https://doi.org/10.31234/osf.io/pt863.

Cusimano, Maddie, Luke B. Hewitt, and Josh H. McDermott. 2024. “Listening with Generative Models.” Cognition 253 (December): 105874. https://doi.org/10.1016/j.cognition.2024.105874.

Gerstenberg, Tobias, and Joshua B Tenenbaum. 2017. “Intuitive Theories.” In Oxford Handbook of Causal Reasoning, edited by Michael Waldmannn, 515–48. Oxford University Press.

Goodman, Noah D, Chris L Baker, Elizabeth Baraff Bonawitz, Vikash K Mansinghka, Alison Gopnik, Henry Wellman, Laura Schulz, and Joshua B Tenenbaum. 2006. “Intuitive Theories of Mind: A Rational Approach to False Belief.” In Proceedings of the Annual Meeting of the Cognitive Science Society. Vol. 28.

Goodman, Noah D, Joshua B. Tenenbaum, and The ProbMods Contributors. 2016. “Probabilistic Models of Cognition.” http://probmods.org/v2.

Houlihan, Sean Dae, Max Kleiman-Weiner, Luke B. Hewitt, Joshua B. Tenenbaum, and Rebecca Saxe. 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, E. T. 2003. Probability Theory: The Logic of Science. Edited by G. Larry Bretthorst. 1st ed. Cambridge University Press. https://doi.org/10.1017/CBO9780511790423.

Marr, David. 1982. Vision: A Computational Investigation into the Human Representation and Processing of Visual Information. San Francisco: W.H. Freeman.

McElreath, Richard. 2016. Statistical Rethinking: A Bayesian Course with Examples in R and Stan. Chapman & Hall/CRC Texts in Statistical Science Series 122. Boca Raton: CRC Press/Taylor & Francis Group.

Moon, Katie, and Deborah Blackman. 2014. “A Guide to Understanding Social Science Research for Natural Scientists.” Conservation Biology 28 (5): 1167–77. https://doi.org/10.1111/cobi.12326.

Poldrack, Russell A. 2021. “The Physics of Representation.” Synthese 199 (1): 1307–25. https://doi.org/10.1007/s11229-020-02793-y.

Tenenbaum, Joshua B, Charles Kemp, Thomas L Griffiths, and Noah D Goodman. 2011. “How to Grow a Mind: Statistics, Structure, and Abstraction.” Science 331 (6022): 1279–85. https://doi.org/10.1126/science.1192788.

Varela, Francisco J., Evan Thompson, and Eleanor Rosch. 1991. The Embodied Mind: Cognitive Science and Human Experience. The Embodied Mind: Cognitive Science and Human Experience. Cambridge, MA, US: The MIT Press.