Spring 2025 | Yale University
This course will look at central issues in the cognitive science of human cognition. Topics we’ll cover include how and when the mind models the outside world, what kinds of cognitive processes are rational, and what it takes for a computational system to be agentic. We will approach these problems through the lens of artificial intelligence, making the philosophical questions concrete by asking how they apply to concrete computational systems.
General
Readings
Day 1, January 14: Is Artificial Intelligence Possible?
[\a] Turing, Alan M. (1950). “Computing Machinery and Intelligence.” Mind, 59(236): 433–460.
Day 2, January 21: Approaches to Computational Cognitive Science
[\a] Griffiths, Thomas L., Joshua B. Tenenbaum, & Charles Kemp. (2010). “Probabilistic models of cognition: Exploring representations and inductive biases.” Trends in Cognitive Sciences, 14(8): 357–364.
[\a] McClelland, James L., David E. Rumelhart, & Geoffrey E. Hinton. (2010). “Letting structure emerge: Connectionist and dynamical systems approaches to cognition.” Trends in Cognitive Sciences, 14(8): 348–356.
Day 3, January 28: What Is the Computational Theory of Mind? (Part I)
[\a] Lake, Brenden M., Ruslan Salakhutdinov, & Joshua B. Tenenbaum. (2015). “Human-level concept learning through probabilistic program induction.” Science, 350(6266): 1332–1338.
[\a] Pylyshyn, Zenon W. (1986). Computation and Cognition: Toward a Foundation for Cognitive Science. Cambridge, MA: MIT Press. Preface (pp. xi–xxi) and Chapters 1–2.
Day 4, February 4: What Is the Computational Theory of Mind? (Part II)
[\c] Gallistel, C. R., & Adam Philip King. (2004). Memory and the Computational Brain: Why Cognitive Science Will Transform Neuroscience. Malden, MA: Blackwell. Chapter 4.
Day 5, February 11: Bayesian Views of Concepts
[\a] Goodman, Noah D., Joshua B. Tenenbaum, & Thomas L. Griffiths. (2014). “Concepts in a probabilistic language of thought.” Trends in Cognitive Sciences, 18(7): 342–352.
[\o] Icard, Thomas F., & Noah D. Goodman. (2015). “A resource-rational approach to the causal frame problem.” Proceedings of the Annual Meeting of the Cognitive Science Society.
Day 6, February 18: Reasoning with Mental Models
[\o] Icard, Thomas F., & Noah D. Goodman. (2015). “A resource-rational approach to the causal frame problem.” (Cont’d from last session)
Collins, et al. (Manuscript). “Human-like causal reasoning with model synthesis architectures.”
Day 7, February 25: Reasoning and Language Model Reasoning
[\o] Zelikman, Eric, et al. (2022). “STaR: Bootstrapping reasoning with reasoning.” Advances in Neural Information Processing Systems.
[\o] Gandhi, Kanishka, et al. (2024). “Stream of Search (SoS): Learning to search in language.” arXiv preprint.
[\o] Zelikman, Eric, et al. (2024). “Quiet-STaR: Language models can teach themselves to think before speaking.” arXiv preprint.
Day 8, March 4: What Is an Agent?
[\a] Bratman, Michael E., David J. Israel, & Martha E. Pollack. (1988). “Plans and resource-bounded practical reasoning.” Computational Intelligence, 4(4): 349–355.
[\a] Bratman, Michael E. (2022). “A planning agent’s self-governance over time.” Philosophical Issues, 32(1): 1–23.
[\o] Park, Joon Sung, et al. (2023). “Generative agents: Interactive simulacra of human behavior.” arXiv preprint.
Day 9, March 25: Rational Agency (Josh Visit)
[\c] Russell, Stuart J., & Peter Norvig. (2021). Artificial Intelligence: A Modern Approach, 4th ed. Hoboken, NJ: Pearson. Chapter “Intelligent Agents,” pp. 36–63.
Day 10, April 1: Thinking Fast and Slow
[\c] Kahneman, Daniel. (2011). Thinking, Fast and Slow. New York: Farrar, Straus and Giroux. Chapters 1–4.
[\o] Gershman, Samuel J., & Noah D. Goodman. (2014). “Amortized inference in probabilistic reasoning.” Proceedings of the Annual Meeting of the Cognitive Science Society.
Day 11, April 8: Possibly: Amortization in Reasoning (Sam Gershman Visit)
[\a] Dasgupta, Ishita, et al. (2020). “A theory of learning to infer.” Psychological Review, 127(3): 412–441.
[\a] Dasgupta, Ishita, et al. (2017). “Where do hypotheses come from?” Cognitive Psychology, 96: 1–25.
Day 12, April 15: Possibly: Small World Problems and Grand World Problems (Dan Greco Visit)
[\c] Harman, Gilbert. Change in View. Cambridge, MA: MIT Press. Chapter 3.
[\c] Christensen, David. Putting Logic in Its Place. Oxford: Oxford University Press. Chapter 2.
[\o] Dennett, Daniel C. (1984). “Cognitive wheels: The frame problem of AI.” In The Philosophy of Artificial Intelligence. Oxford: Oxford University Press.