PSY/COS 360: Computational Models of Cognition

Fall 2026

Course announcements and general questions will be handled through Ed. Problem sets and regrade requests should be submitted through Gradescope; submission links are available on Canvas. Readings are available on Canvas under “Modules”.

Class Times

Monday and Wednesday, 10:40am – 12pm, Jadwin Hall A10

Contact information and Ed discussion

We use Ed Discussion for questions and class discussions. Rather than emailing questions to the teaching staff, please post your questions on Ed Discussion. It will get you a faster response and the answer will benefit others with the same question.

If you have a question that isn’t suitable for Ed Discussion and there is a need to email the teaching staff directly, please use the following email address: instructors-cmc-fall2026@googlegroups.com

Prof (post on Ed for general questions):

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

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

TAs (post on Ed for general questions):

Name	Email/Username	Office Hours	Location
Abby Fergus	`abby.fergus`	Wednesday 1-2 PM	PSH 226
Kristen Ziman	`kz0108`	Thursdays 12-1 PM	PNI 282a
Branson Byers	`jbbyers`	Monday 12:30-1:30 PM	PNI 141
Renata Biazzi	`renata.biazzi`	Mondays 2–3 PM	PSY 116

(PSH = Peretsman Scully Hall, PNI = Princeton Neuroscience Institute)

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. The link will be posted soon.

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
4 homeworks (each involving programming)	50%
Midterm exam (in person)	25%
Final exam (in person)	25%

Problem sets are due at 11:59pm on the days indicated in the syllabus. You get three late days total for problem sets for the entire semester, to account for foreseeable crises. After using these late days, 20% of the total points on the problem set will be deducted for each 24 hours (or portion thereof) that it is late. Start working on the problem sets well in advance of the deadline! Limited collaboration (discussion, but not writing) is encouraged (see below).

Problem sets will be distributed and completed using Jupyter notebooks. More information on how these are distributed will be coming soon.

Exams

There will be an in-person Midterm (Monday, Oct. 12, during class time) and Final exam (Wed, Dec. 16, 4-6 PM). The exams will be a mix of multiple choice and free response. The exam will be closed-book and proctored.

Homework

Homework assignments will be Jupyter Notebooks available for download on the course github. You will submit them via Gradescope, which is linked on Canvas. Please be sure to properly indicate where your response is to each question.

Grading

Final letter grades will roughly correspond to the following point totals:

Grade	Range	Grade	Range	Grade	Range
A+	by discretion	B+	87.0–89.9	C+	77.0–79.9
A	93.0 or higher	B	83.0–86.9	C	73.0–76.9
A-	90.0–92.9	B-	80.0–82.9	C-	70.0–72.9
D	60.0–69.9	F	59.9 and below	—	—

Per university policy, an A+ will be awarded only for exceptional work in the course, which typically requires an A+ on the final project.

If you believe an assignment or exam has received a grade in error, you may submit an appeal. More information about the appeal process is forthcoming.

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!
Research Participation Assignment. Students in this course must complete a Research Participation Assignment. There are two options: students may participate in psychology experiments for course credit, or may opt to complete the alternative research writing assignment. Four hours of experimental participation are assigned to any student in this course who has not already completed 8 hours of participation for other psychology courses in the past. Students must complete the required number of sessions by the last day of Reading Period in order to pass the course. Please refer to the Research Participation Assignment document posted on Cavnas for complete details and instructions. This assignment reflects the psychology department’s belief that experiencing research as a participant adds greatly to a student’s understanding of course material, particularly to the student’s understanding of how psychologists study behavior. Your participation not only furthers your education about the nature of psychological research; it also makes a substantial, critical contribution to psychological research at Princeton and to science in general. As an alternative to research participation, students may complete the research writing assignment. Each paper is worth 0.5 hrs of credit. Eight papers would be required if you choose not to participate in any experiments. Please see the Research Assignment document posted on Cavnas for further details. All questions pertaining to this assignment should be directed to RoseMarie Stevenson (rosemari@princeton.edu).
Collaboration and plagiarism. Programming is an individual creative process much like composition. You must reach your own understanding of the problem and discover a path to its solution. During this time, discussions with other people are permitted and encouraged. However, when the time comes to write code that solves the problem, such discussions (except with course staff members) are no longer appropriate: the code must be your own work. If you have a question about how to use some feature of Python or Jupyter you can certainly ask your friends or the teaching assistants, but specific questions about code you have written must be treated more carefully. For each assignment, you must specifically describe whatever help (if any) you received from others and tell us the names of any individuals with whom you collaborated. This includes help from friends, classmates, and course staff members.

Do not, under any circumstances, copy another person’s code. Incorporating someone else’s code into your program in any form is a violation of academic regulations. This includes adapting solutions or partial solutions to assignments from any offering of this course or any other course. Abetting plagiarism or unauthorized collaboration by “sharing” your code is also prohibited. Sharing code in digital form is an especially egregious violation: do not e-mail your code or make your source files available to anyone.

Novices often have the misconception that copying and mechanically transforming a program (by rearranging independent code, renaming variables, or similar operations) makes it something different. Actually, identifying plagiarized source code is easier than you might think. Not only does plagiarized code quickly identify itself as part of the grading process, but also we can turn to software packages for automatic help.

This policy supplements the University’s academic regulations, making explicit what constitutes a violation for this course. Princeton’s Rights, Rules, Responsibilities handbook asserts:

The only adequate defense for a student accused of an academic violation is that the work in question does not, in fact, constitute a violation. Neither the defense that the student was ignorant of the regulations concerning academic violations nor the defense that the student was under pressure at the time the violation was committed is considered an adequate defense.

If you have questions about these matters, please consult a course staff member. Violators will be referred to the Committee on Discipline for review; if found guilty, you will receive an F as a course grade plus whatever disciplinary action the Committee imposes.
Use of AI on homework assignments. We won’t restrict the use of AI on the homework assignments, but we strongly discourage it. The homeworks are designed as a learning exercise, to be done without AI assitance. The homeworks are not the main assessment or differentiator for grades; before the rise of LLMs, students did very well on the homeworks too. Using AI on the homeworks will hurt you on the midterm and final.
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.

Monday, September 7 — Labor day - No class

Part I: Categorization

Wednesday, September 9 — Lecture 2: Categorization 1

Homework 1 out
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.
Optional: Medin, D., & Schaffer, M. (1978). Context theory of classification. Psychological Review, 85, 207–238.

Precept week of Sept 9

Introduction to Python and Jupyter notebooks

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.

Precept week of Sept 14 and 16

Models of categorization; HW 1 help

Part II: Neural network models

Monday, September 21 — Lecture 5: Neural networks 1

Homework 1 is due. Homework 2 is out
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.

Precept week of Sept 21 and 23

Introduction to Pytorch

Monday, September 28 — Lecture 7: Neural networks 3

Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211.
Optional: 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.
Optional: 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.

Precept week of Sept 28 and 30

HW 2 help

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

Homework 2 is due.
Binz, M., Akata, E., Bethge, M. et al. (2025). A foundation model to predict and capture human cognition. Nature.
Optional: Bhatia, S., & Richie, R. (2024). Transformer networks of human conceptual knowledge. Psychological Review, 131(1), 271.
Optional: 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.
Optional: 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.

Precept week of Oct 5 and 7

Review session for midterm

Monday, October 12 — Midterm exam

Part III: Bayesian models

Wednesday, October 14 — Lecture 11: Bayesian models 1

Homework 3 is out.
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.

Precept week of Oct 12 and 14

Probability review

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

Precept week of Oct 19 and 21 — Fall Break, no precepts

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.
Optional: Anderson, J. R. (1991). The adaptive nature of human categorization. Psychological Review, 98(3), 409.

Precept week of Oct 26 and 28

Bayesian models 1; HW 3 help

Monday, November 2 — Lecture 14: Probabilistic Graphical models

Griffiths, T. L. & Yuille, A. (2024). Graphical models. BMC, Chapter 4.
Optional: 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

Homework 3 is due.
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).
Optional: Lake, B. M., Salakhutdinov, R., & Tenenbaum, J. B. (2015). Human-level concept learning through probabilistic program induction. Science, 350(6266), 1332–1338.

Precept week of Nov 2 and 4

Bayesian models 2; HW 3 help

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.
Optional: Lake, B. M., Jagadish, A., & Jiang, G. (2026). More accurate behavioral predictions with hybrid Bayesian-connectionist models. arXiv preprint.
Optional: 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

Homework 4 is out.
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)
Optional: Feldman, J. (2000). Minimization of Boolean complexity in human concept learning. Nature, 407, 630–633.

Precept week of Nov 9 and 11

Unifying Bayesian models and neural networks

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.
Optional: 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.

Precept week of Nov 16 and 18

HW 4 help

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

Homework 4 is due.
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.
Optional: Gold, E. M. (1967). Language identification in the limit. Information and Control, 10, 447–474.

Wednesday, November 25 — Thanksgiving break – No class

Precept week of Nov 23

Symbolic models

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 by Akshay Jagadish)

Precept week of Nov 30 and Dec 2

Final exam review

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

Wednesday, December 16, 4-6 PM — Final exam