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.

Schedule

Date Topic Reading Assignments
Mon
Jan 6		 Introduction to the course
Wed
Jan 8		 Modeling as philosophy
• ontology and epistemology
• representation and abstraction
• levels of analysis
Mon
Jan 13		 Belief
• intentional stance
• recursive (level-k) beliefs
• false belief
Wed
Jan 15		 Generative models
• Bayes’ rule
• cultures and traditions of modeling
• types of uncertainty
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		 Priors
• divergent explanation
Tue
Jan 21		 PPL Exercise Due
Wed
Jan 22		 Intuitive Theories
• Bayes is generative
  • Gerstenberg & Tenenbaum (2017)
  • Goodman et al. (2006)
Mon
Jan 27		 Causal Models
• how do we know?
Wed
Jan 29		 Causal Motifs
• building blocks and backdoors
Fri
Jan 31		 PPL Exercise Due
Mon
Feb 3		 Causal Analysis
  • Cinelli et al. (2022)
Wed
Feb 5		 Hierarchical models
•  Hierarchical models
Mon
Feb 10		 Intuitive Causal Reasoning
•  from BDA to mental models
  • Lake et al. (2017)
Wed
Feb 12
Mon
Feb 17		 Theory of Mind
Wed
Feb 19		 Inverse Planning
•  Expected utility maximization
•  Prospect theory
  • Baker et al. (2017)
  • Rabinowitz et al. (2018)
  • Zhi-Xuan et al. (2022)
  • Gandhi et al. (2021)
  • Tutorial: Inverse planning
  • Tutorial: POMDPs
  • Tutorial: Factorization
Fri
Feb 21		 Project Proposal Due
Mon
Feb 24		 Emotion Prediction
  • Houlihan et al. (2023)
  • Thornton & Tamir (2017)
  • Tutorial: Reputation management
  • Tutorial: Emotion prediction
Wed
Feb 26		 Emotion Recognition & Emotion Reasoning
  • Houlihan et al. (2022)
  • Ong et al. (2015)
  • Cowen & Keltner (2020)
  • Tutorial: Abductive inference
  • Tutorial: Bayesian cue integration
Mon
Mar 3		 Learning Probabilistic Programs
  • Lake et al. (2015)
  • Hewitt et al. (2020)
  • Tutorial: Bayesian program learning
  • Tutorial: Amortization, memoization
Wed
Mar 5		 Neuro-Symbolic Probabilistic Program Synthesis
  • Ellis et al. (2023)
  • Wong et al. (2023)
  • Goodman et al. (2023)
Mon
Mar 10
11:30AM
Kemeny 108		 Final Project 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).

    Citation

    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 and Tony Chen building memo, and to Kartik for adapting memo for this course.

References

Baker, Chris L., Jara-Ettinger, Julian, Saxe, Rebecca, & Tenenbaum, Joshua B. (2017). Rational quantitative attribution of beliefs, desires and percepts in human mentalizing. Nature Human Behaviour, 1(4), 598. https://doi.org/10.1038/s41562-017-0064
Chandra, Kartik, Chen, Tony, Tenenbaum, Joshua, & Ragan-Kelley, Jonathan. (2025, January 9). A Domain-Specific Probabilistic Programming Language for Reasoning About Reasoning (or: A memo on memo). https://doi.org/10.31234/osf.io/pt863
Cinelli, Carlos, Forney, Andrew, & Pearl, Judea. (2022). A Crash Course in Good and Bad Controls. Sociological Methods & Research, 00491241221099552. https://doi.org/10.1177/00491241221099552
Cowen, Alan S., & Keltner, Dacher. (2020). What the face displays: Mapping 28 emotions conveyed by naturalistic expression. American Psychologist, 75(3), 349–364. https://doi.org/10.1037/amp0000488
Cusimano, Maddie, Hewitt, Luke B., & McDermott, Josh H. (2024). Listening with generative models. Cognition, 253, 105874. https://doi.org/10.1016/j.cognition.2024.105874
Ellis, Kevin, Wong, Lionel, Nye, Maxwell, Sablé-Meyer, Mathias, Cary, Luc, Anaya Pozo, Lore, Hewitt, Luke, Solar-Lezama, Armando, & Tenenbaum, Joshua B. (2023). DreamCoder: Growing generalizable, interpretable knowledge with wake–sleep Bayesian program learning. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 381(2251), 20220050. https://doi.org/10.1098/rsta.2022.0050
Gandhi, Kanishk, Stojnic, Gala, Lake, Brenden M., & Dillon, Moira R. (2021). Baby Intuitions Benchmark (BIB): Discerning the goals, preferences, and actions of others. In M. Ranzato, A. Beygelzimer, Y. Dauphin, P. S. Liang, & J. Wortman Vaughan (Eds.), Advances in neural information processing systems (Vol. 34, pp. 9963–9976). Curran Associates, Inc. https://proceedings.neurips.cc/paper/2021/file/525b8410cc8612283c9ecaf9a319f8ed-Paper.pdf
Gelman, Andrew. (2014). Bayesian data analysis (Third edition). CRC Press.
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., Baker, Chris L., Bonawitz, Elizabeth Baraff, Mansinghka, Vikash K., Gopnik, Alison, Wellman, Henry, Schulz, Laura, & Tenenbaum, Joshua B. (2006). Intuitive theories of mind: A rational approach to false belief. Proceedings of the Annual Meeting of the Cognitive Science Society, 28.
Goodman, Noah D., Gerstenberg, Tobias, & Tenenbaum, Joshua B. (2023). Probabilistic programs as a unifying language of thought. In Nick Chater & Joshua Tenenbaum (Eds.), Bayesian models of cognition: Reverse engineering the mind. MIT Press.
Goodman, Noah D., Tenenbaum, Joshua B., & Contributors, The ProbMods. (2016). Probabilistic models of cognition (Second). http://probmods.org/v2
Hewitt, Luke B., Anh Le, Tuan, & Tenenbaum, Joshua B. (2020). Learning to learn generative programs with Memoised Wake-Sleep. In Jonas Peters & David Sontag (Eds.), Proceedings of the 36th conference on uncertainty in artificial intelligence (UAI) (Vol. 124, pp. 1278–1287). PMLR. https://proceedings.mlr.press/v124/hewitt20a.html
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
Houlihan, Sean Dae, Ong, Desmond, Cusimano, Maddie, & Saxe, Rebecca. (2022). Reasoning about the antecedents of emotions: Bayesian causal inference over an intuitive theory of mind. Proceedings of the Annual Meeting of the Cognitive Science Society, 44, 854–861. https://escholarship.org/uc/item/7sn3w3n2
Jaynes, E. T. (2003). Probability Theory: The Logic of Science (G. Larry Bretthorst, Ed.; 1st ed.). Cambridge University Press. https://doi.org/10.1017/CBO9780511790423
Kruschke, John K. (2015). Doing Bayesian data analysis: A tutorial with R, JAGS, and Stan (Edition 2). Academic Press.
Lake, Brenden M., Salakhutdinov, Ruslan, & Tenenbaum, Joshua B. (2015). Human-level concept learning through probabilistic program induction. Science, 350(6266), 1332–1338. https://doi.org/10.1126/science.aab3050
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
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
Ong, Desmond C., Zaki, Jamil, & Goodman, Noah D. (2015). Affective cognition: Exploring lay theories of emotion. Cognition, 143, 141–162. https://doi.org/10.1016/j.cognition.2015.06.010
Pearl, Judea. (2021). Causal and Counterfactual Inference. In Markus Knauff & Wolfgang Spohn (Eds.), The Handbook of Rationality (pp. 427–438). The MIT Press. https://doi.org/10.7551/mitpress/11252.003.0044
Phillips, Jonathan, Buckwalter, Wesley, Cushman, Fiery, Friedman, Ori, Martin, Alia, Turri, John, Santos, Laurie, & Knobe, Joshua. (2021). Knowledge before belief. Behavioral and Brain Sciences, 44, 1–37. https://doi.org/10.1017/S0140525X20000618
Poldrack, Russell A. (2021). The physics of representation. Synthese, 199(1), 1307–1325. https://doi.org/10.1007/s11229-020-02793-y
Rabinowitz, Neil, Perbet, Frank, Song, Francis, Zhang, Chiyuan, Eslami, S. M. Ali, & Botvinick, Matthew. (2018). Machine theory of mind. In Jennifer Dy & Andreas Krause (Eds.), Proceedings of the 35th international conference on machine learning (Vol. 80, pp. 4218–4227). PMLR. https://proceedings.mlr.press/v80/rabinowitz18a.html
Sap, Maarten, LeBras, Ronan, Fried, Daniel, & Choi, Yejin. (2023, April 3). Neural Theory-of-Mind? On the Limits of Social Intelligence in Large LMs. http://arxiv.org/abs/2210.13312
Saxe, Rebecca. (2005). Against simulation: The argument from error. Trends in Cognitive Sciences, 9(4), 174–179. https://doi.org/10.1016/j.tics.2005.01.012
Shea, Nicholas. (2018). Representation in Cognitive Science (Vol. 1). Oxford University Press. https://doi.org/10.1093/oso/9780198812883.001.0001
Tenenbaum, Joshua B., Kemp, Charles, Griffiths, Thomas L., & Goodman, Noah D. (2011). How to Grow a Mind: Statistics, Structure, and Abstraction. Science, 331(6022), 1279–1285. https://doi.org/10.1126/science.1192788
Thornton, Mark A., & Tamir, Diana I. (2017). Mental models accurately predict emotion transitions. Proceedings of the National Academy of Sciences, 114(23), 5982–5987. https://doi.org/10.1073/pnas.1616056114
Ullman, Tomer. (2023, February 16). Large Language Models Fail on Trivial Alterations to Theory-of-Mind Tasks. http://arxiv.org/abs/2302.08399
Varela, Francisco J., Thompson, Evan, & Rosch, Eleanor. (1991). The embodied mind: Cognitive science and human experience. (pp. xx, 308). The MIT Press.
Wong, Lionel, Grand, Gabriel, Lew, Alexander K., Goodman, Noah D., Mansinghka, Vikash K., Andreas, Jacob, & Tenenbaum, Joshua B. (2023, June 23). From Word Models to World Models: Translating from Natural Language to the Probabilistic Language of Thought. http://arxiv.org/abs/2306.12672
Zhi-Xuan, Tan, Gothoskar, Nishad, Pollok, Falk, Gutfreund, Dan, Tenenbaum, Joshua B., & Mansinghka, Vikash K. (2022, August 4). Solving the Baby Intuitions Benchmark with a Hierarchically Bayesian Theory of Mind. https://doi.org/10.48550/ARXIV.2208.02914