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’ rule Tutorial: Bayes’ rule
Fri
Jan 23
5:30-7:20pm
NB Special Date & Time		 Bayes is generative
Mon
Jan 26		 Modeling as philosophy
Wed
Jan 28		 Intuitive Theories
  • Gerstenberg & Tenenbaum (2017)
  • Lake et al. (2017)
Fri
Jan 30		 PPL Exercise Due
Mon
Feb 2		 Causal models
Wed
Feb 4		 Dependence
Mon
Feb 9		 Causal motifs
Wed
Feb 11		 Conditional dependence and intervention
Mon
Feb 16		 Causal Analysis
  • Cinelli et al. (2022)
  • Chandra et al. (n.d.)
Wed
Feb 18		 Theory of Mind
Fri
Feb 20		 PPL Exercise Due
Mon
Feb 23		 Inverse Planning
  • Baker et al. (2017)
  • Rabinowitz et al. (2018)
  • Zhi-Xuan et al. (2022)
  • Gandhi et al. (2021)
Wed
Feb 25		 No Class
Fri
Feb 27		 PPL Exercises Due
Mon
Mar 2		 Emotion Prediction, Emotion Inference
  • Houlihan et al. (2023)
  • Houlihan et al. (2022)
  • Thornton & Tamir (2017)
  • Cowen & Keltner (2020)
  • Ong et al. (2015)
Wed
Mar 4		 Learning Probabilistic Programs
  • Lake et al. (2015)
  • Hewitt et al. (2020)
Mon
Mar 9		 Neuro-Symbolic Probabilistic Program Synthesis
  • Ellis et al. (2023)
  • Wong et al. (2025)
  • Goodman et al. (2023)
  • Wong et al. (2023)
Fri
Mar 13
3pm
NB Date & Time

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

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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
Chandra, Kartik, Ragan-Kelley, Jonathan, & Tenenbaum, Joshua B. (n.d.). Theories of Mind as Languages of Thought for Thought about Thought.
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
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