COS 597B Advanced Topics in Computer Science - Computational Models of Cognition

Fall 2026

Course announcements and general questions will be handled through Ed. Readings are available on Canvas under “Modules”.

Class Times

Lecture: Monday and Wednesday, 10:40am – 12pm, Jadwin Hall A10
Discussion: Wednesday, 1:20-2:10pm, Friend Center 112

Contact information and Ed discussion

We use Ed Discussion for course questions and weekly respones to the readings.

Prof (post on Ed for general questions):

Name	Email/Username	Office Hours	Location
Brenden Lake	`brenden`	Tuesday 4–5 PM	Peretsman Scully Hall 117

Note, Sept. 1 Tuesday 4-4:30 PM ONLY

TA (post on Ed for general questions):

Name	Email/Username	Office Hours	Location
Phoebe Zeng	`pk1124`	Friday 2-3 PM	Computer Science 003

Course Summary

The objective of this course is to provide advanced students in cognitive science, psychology, and computer science with the skills to develop computational models of human cognition. Computational modeling is one of the central methods in cognitive science research, and can help to provide insight into how people solve the challenging problems posed by everyday life, as well as how to bring computers closer to human performance for some of these problems. The course will explore three ways in which researchers have attempted to formalize cognition — neural networks, Bayesian models, and symbolic approaches — considering the strengths and weaknesses of each.

Who Should Take This Course

The course is designed for advanced students in cognitive science, psychology, or computer science who are interested in developing computational models of cognition. Prerequisites are skills with programming (we will use Python in the course problem sets), comfort with talking about ideas from elementary calculus, linear algebra, and probability theory, and an interest in cognitive science.

Readings

Background readings will be drawn from:

Russell, S., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach. (4th ed.) Upper Saddle River, NJ: Prentice-Hall.

This book is referred to as AIMA4 in the rest of the syllabus. It is okay to use the previous edition of this textbook if necessary. This book will be referred to as AIMA3 for giving page numbers, etc.

We will also use material from:

Griffiths, T. L., Chater, N., & Tenenbaum, J. B. (Eds.). (2024). Bayesian Models of Cognition: Reverse Engineering the Mind. MIT Press.

This book is referred to as BMC in the rest of the syllabus and is available online here.

Many classes also use primary sources in cognitive science, which will be available as PDF files on Canvas. The optional readings are listed for your interest only, and provide a feel for the of the discipline.

Finally, the perfect companion book for this class is:

Griffiths, T. (2026). The Laws of Thought: The Quest for a Mathematical Theory of the Mind. HarperCollins UK.

This book walks through the three main paradigms that organize this class, with a focus on their history and key developments.

Course Requirements

Requirement	Percentage of Final Grade
Responses to readings	30%
Participation	10%
Final project	60%

Reactions to readers should be a couple of paragraphs inspired by the readings for the week. They can raise a question that came up for you when reading (and give your thoughts about it), point to related ideas, or highlight a thought you had about the topic. Notes should be submitted by midnight on Tuesday for the following class. No AI is allowed in drafting your responses, either the ideas or the text itself. We want your raw thoughts, ideas, and reactions, not polished text or AI ideas.

Final project guidelines

Please see Canvas for the details of the final project specifications.

Proposal is due Wednesday, October 14, midnight
Final project is due Wednesday, December 16, midnight

Course Policies

Classes start on time. Important administrative announcements will sometimes be made at the start of class. Please make sure to come with enough time in advance so that you don’t miss anything!
Use of AI assistants on responses to readings. No AI is allowed in drafting your responses, either the ideas or the text itself. We want your raw thoughts and reactions, not polishsed text or AI ideas.
Use of AI assistants on projects. Please check back soon for our policy.
Laptops in class. Laptops in class are discouraged unless they are needed for a specific reason. We know many try to take notes on their laptops, but it’s easy to get distracted (social media, etc.). This also distracts everyone behind you. We encourage you to engage with the class and material, and engage with us as the instructors. Ask questions! All slides are posted so there is no need to copy things down, and paper notes are great too.
No extra credit. No extra credit will be given, beyond clearly marked extra questions on exams, out of interest of fairness.
Accommodations. Contact the instructor for accommodation of religious beliefs, disabilities, and other special circumstances. Students must register with the Office of Disability Services (ODS) (ods@princeton.edu; 258-8840) for disability verification and determination of eligibility for reasonable academic accommodations. Requests for academic accommodations for this course need to be made at the beginning of the semester, or as soon as possible for newly approved students, and again at least two weeks in advance of any needed accommodations in order to make arrangements to implement the accommodations. Please make an appointment to meet with the instructor in order to maintain confidentiality in addressing your needs. No accommodations will be given without authorization from ODS, or without advance notice.

Schedule

Readings are available on Canvas under “Modules”

Wednesday, September 2 — Lecture 1: Introduction

Marr, D. (1982). Vision. San Francisco: W. H. Freeman. Chapter 1.

Wednesday, September 2 — Discussion: Introduce yourself and your interests.

Monday, September 7 — Labor day - No class

Part I: Categorization

Wednesday, September 9 — Lecture 2: Categorization 1

Murphy, G. L. (2003). The Big Book of Concepts. Cambridge, MA: MIT Press. Chapters 2 and 3.
Rosch, E., & Mervis, C. (1975). Family resemblances: Studies in the internal structure of categories. Cognitive Psychology, 7, 573–605.
Medin, D., & Schaffer, M. (1978). Context theory of classification. Psychological Review, 85, 207–238.

Wednesday, September 9 — Discussion of lectures and readings for the week

Monday, September 14 — Lecture 3: Categorization 2

Shepard, R. N. (1987). Toward a universal law of generalization for psychological science. Science, 237, 1317–1323.

Wednesday, September 16 — Lecture 4: Categorization 3

Jäkel, F., Schölkopf, B., & Wichmann, F. A. (2008). Generalization and similarity in exemplar models of categorization: Insights from machine learning. Psychonomic Bulletin & Review, 15, 256-271.

Wednesday, September 16 — Discussion of lectures and readings for the week

Part II: Neural network models

Monday, September 21 — Lecture 5: Neural networks 1

Marr, D. (1982). Vision. San Francisco: W. H. Freeman. Chapter 1.
McClelland, J. L., Rumelhart, D. E., & Hinton, G. E. (1986). The Appeal of Parallel Distributed Processing. Vol I, Ch 1.
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521, 436–444.

Wednesday, September 23 — Lecture 6: Neural networks 2

McClelland, J. L., & Rogers, T. T. (2003). The parallel distributed processing approach to semantic cognition. Nature Reviews Neuroscience, 4(4), 310–322.
Kruschke, J. L. (1992). ALCOVE: An exemplar-based connectionist model of category learning. Psychological Review, 99, 22–44.

Wednesday, September 23 — Discussion of lectures and readings for the week

Monday, September 28 — Lecture 7: Neural networks 3

Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211.
Botvinick, M. M., & Plaut, D. C. (2006). Short-term memory for serial order: A recurrent neural network model. Psychological Review, 113(2), 201.

Wednesday, September 30 — Lecture 8: Neural networks 4

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems (pp. 1097–1105).
Peterson, J., Abbott, J., & Griffiths, T. (2016). Adapting deep network features to capture psychological representations. Presented at the 38th Annual Conference of the Cognitive Science Society.
Orhan, A. E., & Lake, B. M. (2024). Learning high-level visual representations from a child’s perspective without strong inductive biases. Nature Machine Intelligence, 6, 271–283.

Wednesday, September 30 — Discussion of lectures and readings for the week

Monday, October 5 — Lecture 9: LLM models of cognition 1

Binz, M., Akata, E., Bethge, M. et al. (2025). A foundation model to predict and capture human cognition. Nature.
Bhatia, S., & Richie, R. (2024). Transformer networks of human conceptual knowledge. Psychological Review, 131(1), 271.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., … & Polosukhin, I. (2017). Attention is all you need. Advances in neural information processing systems, 30.

Wednesday, October 7 — Lecture 10: LLM models of cognition 2

Webb, T., Holyoak, K.J. & Lu, H. (2023). Emergent analogical reasoning in large language models. Nature Human Behavior, 7, 1526–1541.
Bubeck, S., Chandrasekaran, V., Eldan, R., Gehrke, J., Horvitz, E., Kamar, E., et al. (2023). Sparks of artificial general intelligence: Early experiments with GPT-4. arXiv preprint arXiv:2303.12712.

Wednesday, October 7 — Discussion of lectures and readings for the week

Monday, October 12 — No class — PSY/COS 360 midterm during this slot

Part III: Bayesian models

Wednesday, October 14 — Lecture 11: Bayesian models 1

Griffiths, T. L. & Tenenbaum, J. B. (2024). Bayesian inference. BMC, Chapter 3.
Tenenbaum, J. B., & Griffiths, T. L. (2001). Generalization, similarity, and Bayesian inference. Behavioral and Brain Sciences, 24, 629–641.

Wednesday, October 14 — Discussion of lectures and readings for the week

Final project proposal is due

Monday, October 19 & Wednesday, October 21 — Fall Break - No class

Monday, October 26 — Lecture 12: Bayesian models 2

Tenenbaum, J. B., Kemp, C., Griffiths, T. L., & Goodman, N. D. (2011). How to grow a mind: Statistics, structure, and abstraction. Science, 331(6022), 1279-1285.
MacKay, D. (2003). Chapter 29: Monte Carlo Methods. In Information Theory, Inference, and Learning Algorithms.

Wednesday, October 28 — Lecture 13: Bayesian models 3

Goodman, N. D., Tenenbaum, J. B., Feldman, J., & Griffiths, T. L. (2008). A rational analysis of rule‐based concept learning. Cognitive Science, 32(1), 108-154.
Anderson, J. R. (1991). The adaptive nature of human categorization. Psychological Review, 98(3), 409.

Wednesday, October 28 — Discussion of lectures and readings for the week

Monday, November 2 — Lecture 14: Probabilistic Graphical models

Griffiths, T. L. & Yuille, A. (2024). Graphical models. BMC, Chapter 4.
Kemp, C., & Tenenbaum, J. B. (2008). The discovery of structural form. Proceedings of the National Academy of Sciences, 105(31), 10687-10692.

Wednesday, November 4 — Lecture 15: Program induction and language of thought models

Ghahramani, Z. (2015). Probabilistic machine learning and artificial intelligence. Nature, 521(7553), 452.
Goodman, N. D., Tenenbaum, J. B., & Gerstenberg, T. (2014). Concepts in a probabilistic language of thought. Center for Brains, Minds and Machines (CBMM).
Lake, B. M., Salakhutdinov, R., & Tenenbaum, J. B. (2015). Human-level concept learning through probabilistic program induction. Science, 350(6266), 1332–1338.

Wednesday, November 4 — Discussion of lectures and readings for the week

Monday, November 9 — Lecture 16: Unifying Bayesian models and neural networks

Griffiths, T. L., Lake, B. M., McCoy, R. T., Pavlick, E., & Webb, T. W. (2025). Whither symbols in the era of advanced neural networks?Trends in Cognitive SCience.
Lake, B. M., Jagadish, A., & Jiang, G. (2026). More accurate behavioral predictions with hybrid Bayesian-connectionist models. arXiv preprint.
McCoy, R. T., & Griffiths, T. L. (2025). Modeling rapid language learning by distilling Bayesian priors into artificial neural networks. Nature Communications, 16, 4676.

Part IV: Symbols and rules; historical foundations

Wednesday, November 11 — Lecture 17: Formal systems and propositional logic

AIMA4 or AIMA3, Section 7.4.
Haugeland, J. (1997). What is mind design? In J. Haugeland (Ed.) Mind Design II: Philosophy, Psychology, Artificial Intelligence. Cambridge, MA: MIT Press. (only pages 8–21)
Feldman, J. (2000). Minimization of Boolean complexity in human concept learning. Nature, 407, 630–633.

Wednesday, November 11 — Discussion of lectures and readings for the week

Monday, November 16 — Lecture 18: Production systems and cognitive architectures

Anderson, J. R. (1996). ACT: A simple theory of complex cognition. American Psychologist, 51, 355–365.
Newell, A., Rosenbloom, P. S., & Laird, J. E. (1989). Symbolic architectures for cognition. In M. I. Posner (Ed.), Foundations of Cognitive Science, 93–131. Cambridge, MA: MIT Press.
Newell, A., & Simon, H. (1956). The logic theory machine — A complex information processing system. IRE Transactions on Information Theory, 2, 61–79.

Wednesday, November 18 — Lecture 19: Language as a formal system

AIMA4, pages 833–835, or AIMA3, pages 888–892.
Chomsky, N. (1957). Syntactic Structures. The Hague: Mouton. Pages 11–48.

Wednesday, November 18 — Discussion of lectures and readings for the week

Monday, November 23 — Lecture 20: Learning and the poverty of the stimulus

Pinker, S. (1979). Formal models of language learning. Cognition, 7, 217–283. (only pages 217–234)
Johnson, K. (2004). Gold’s theorem and cognitive science. Philosophy of Science, 71, 571–592.
Gold, E. M. (1967). Language identification in the limit. Information and Control, 10, 447–474.

Wednesday, November 25 — Thanksgiving break – No class

Monday, November 30 — Lecture 21: Unifying symbols, probabilities, and networks

Part V: The End

Wednesday, December 2 — Lecture 22: Auto-experimentation in cognitive science (guest lecture)

Wednesday, December 2 — Discussion: Presentations of final projects

Monday, December 7 — Lecture 23: Course speedrun and AMA

Wednesday, December 16, midnight — Final project due