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.


Canvas (readings, lectures, etc.)

Ed Discussion (forum)


Date Topic Reading PPL Exercise
Week 1      
Jan 4
Goals of the course    
Week 2      
Jan 9
Modeling as philosophy - Marr Ch. 1 (1.1-1.2)
- Tenenbaum JB, Kemp C, Griffiths TL & Goodman ND. (2011). How to Grow a Mind: Statistics, Structure, and Abstraction. Science.
- Varela Ch. 1
Jan 11
Generative models - Cusimano M, Hewitt LB & McDermott JH. (2023). Bayesian auditory scene synthesis explains human perception of illusions and everyday sounds [Preprint].
- Poldrack RA. (2021). The physics of representation. Synthese.
Generative models
Week 3      
Jan 16
From Bayesian statistics to epistemological theory Ibid.  
Jan 18
Intuitive theories - Lake BM, Ullman TD, Tenenbaum JB & Gershman SJ. (2017). Building machines that learn and think like people. Behavioral and Brain Sciences.
- Gerstenberg T & Tenenbaum JB. (2017). Intuitive Theories. In M. Waldmannn (Ed.), Oxford handbook of causal reasoning (pp. 515–548).
Week 4      
Jan 23
Intuitive theories - what do we know?
lecture notes
- McElreath, Chapter 6. The Haunted DAG & The Causal Terror
- Pearl J. (2021). Causal and Counterfactual Inference. In M. Knauff & W. Spohn (Eds.), The Handbook of Rationality (pp. 427–438). The MIT Press.
Jan 25
Causal models - how do we know?
lecture notes (WebPPL)
lecture notes (Turing.jl)
Week 5      
Jan 30
Causal motifs - conservation of belief, patterns of explanation    
Feb 1
Causal analysis - backdoors, do-calculus, adjustment sets - Cinelli C, Forney A & Pearl J. (2022). A Crash Course in Good and Bad Controls. Sociological Methods & Research. Conditional Dependence
Week 6      
Feb 6
Intuitive causal reasoning - from BDA to mental models
lecture notes
Feb 8
Theory of Mind - Saxe R. (2005). Against simulation: the argument from error. Trends in Cognitive Sciences.
- Phillips J, Buckwalter W, Cushman F, Friedman O, Martin A, Turri J, Santos L & Knobe J. (2021). Knowledge before belief. Behavioral and Brain Sciences.
- Sap M, LeBras R, Fried D & Choi Y. (2023). Neural Theory-of-Mind? On the Limits of Social Intelligence in Large LMs. arXiv.
Week 7 Individual meetings about project proposals    
Feb 13
Inference   Social Cognition
Feb 15
Inverse planning - Baker CL, Jara-Ettinger J, Saxe R & Tenenbaum JB. (2017). Rational quantitative attribution of beliefs, desires and percepts in human mentalizing. Nature Human Behaviour.
- Rabinowitz N, Perbet F, Song F, Zhang C, Eslami SMA & Botvinick M. (2018). Machine theory of mind. ICML.
Week 8 Project proposals due Wed Feb 21 @ noon    
Feb 20
Emotion Reasoning, Emotion Recognition - Houlihan SD, Ong DC, Cusimano M & Saxe R. (2023). Causal inference over an intuitive theory of emotion.
- Ong DC, Zaki J & Goodman ND. (2015). Affective cognition: Exploring lay theories of emotion. Cognition.
- Cowen AS & Keltner D. (2020). What the face displays: Mapping 28 emotions conveyed by naturalistic expression. American Psychologist.
Feb 22
Emotion Prediction - Houlihan SD, Kleiman-Weiner M, Hewitt LB, Tenenbaum JB & Saxe R. (2023). Emotion prediction as computation over a generative theory of mind. Philosophical Transactions of the Royal Society A.
- Thornton MA & Tamir DI. (2017). Mental models accurately predict emotion transitions. Proceedings of the National Academy of Sciences.
Week 9      
Feb 27
Probabilistic language of thought - Goodman ND, Gerstenberg T & Tenenbaum JB. (2023). Probabilistic programs as a unifying language of thought. MIT Press.  
Feb 28
Final Class!
Neuro-symbolic probabilistic program synthesis
- Lake BM, Salakhutdinov R & Tenenbaum JB. (2015). Human-level concept learning through probabilistic program induction. Science.
- Hewitt LB, Anh Le T & Tenenbaum JB. (2020). Learning to learn generative programs with Memoised Wake-Sleep. UAI.
March 9
3pm Final Project Presentations    


  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


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

The ProbMods Contibutors are:
Noah D. Goodman (editor)
Joshua B. Tenenbaum
Daphna Buchsbaum
Joshua Hartshorne
Robert Hawkins
Timothy J. O’Donnell
Michael Henry Tessler