Computational cognitive modeling - Fall 2026
This course surveys the leading computational frameworks for understanding human intelligence and cognition. Both psychologists and data scientists are working with increasingly large quantities of human behavioral data. Computational cognitive modeling aims to understand behavioral data, and the mind and brain more generally, by building computational models of the cognitive processes that produce the data. The lectures cover artificial neural networks (deep learning), reinforcement learning, Bayesian modeling, model comparison and fitting, categorization, structured probabilistic models, and program induction, with examples spanning learning, categorization, language, memory, decision-making, and reasoning. Students leave the course with a richer understanding of how computational modeling advances cognitive science, how cognitive science can inform research in machine learning and AI, and how to fit and evaluate cognitive models against behavioral data.
🗓️ At a glance
| 🔢 Course numbers | DS-GA 1016 (Data Science) and PSYCH-GA 3405.004 (Psychology) |
| 🎓 Lectures | Thursday 2:45PM-4:45PM in 194 Mercer St Room 204. No Zoom or lecture capture; if you cannot make it to class, email us to request last year's video. |
| 🔬 Labs | Monday 1:30 PM - 2:20PM in 60 Fifth Ave Room 110. Labs are recorded. |
| 📝 Final exam period | Thursday, December 17, 2026, 2:00PM-3:50PM in TBD. There is no final exam; this slot is the final project poster session. |
| 💬 Ed Discussion | https://edstem.org/us/courses/104832/discussion |
| 📧 Course email | instructors-ccm-fall2026@gureckislab.org |
| ☁️ JupyterHub | https://dsga-1016-fall.rcnyu.org/ |
🧑🏫 Teaching staff
| Name | Role | Office hours | Office | |
|---|---|---|---|---|
| Todd Gureckis | Instructor | Mondays 11am-12pm | Meyer 590 and CDS 708 | tg35@nyu.edu |
| Wentao Wang | Teaching assistant | Mondays 2:30pm-3:30pm | CDS building 765 | wentao.wang@nyu.edu |
Office hours are in person by default. Email us if you need to meet over Zoom instead.
💬 How to reach us
The class Ed Discussion page is the main point of contact. Rather than emailing questions to the teaching staff, please post them on Ed Discussion. You will get a faster response and the answer will benefit others with the same question. Enrolled students should have access automatically. If you do not, a signup link is available here: https://edstem.org/us/join/H8FM8r.
If you have a question that is not suitable for Ed Discussion and you need to email the teaching staff directly, write to instructors-ccm-fall2026@gureckislab.org. This address reaches the whole teaching team.
Brightspace access for waitlist and auditors
We will not use NYU's Brightspace for anything except lab recordings. If you need Brightspace access and do not have it, add your email to this spreadsheet. We add emails from the spreadsheet periodically.
Please note that this syllabus is not final and there may be further adjustments.
✅ Prerequisites
| Area | What we assume | If you need a refresher |
|---|---|---|
| Math | Linear algebra, calculus, and probability at the level of an undergraduate course or the psychology department's Math Tools sequence. | The labs review the basic technical concepts as they come up. |
| Programming | Previous experience with Python is required, and previous in-class experience with Python is strongly recommended. Assignments use Python 3 and Jupyter Notebooks. | The first lab reviews Python, Jupyter, and PyTorch. See the course videos. |
💯 Grading
The final grade is based on:
- 📝 In-class exams (50%): Four exams conducted in class without internet devices or mobile computing devices. Each exam is worth 12.5% of the final grade.
- 📚 Python programming homeworks (15%): Four homework assignments.
- 🎯 Final project (30%): See details below.
- 🙋 Attendance (5%): In-person attendance, checked periodically. See the attendance policy below.
Class participation may be used to decide grades in borderline cases.
🙋 Attendance
In-person attendance is required for this course. Being in person allows you to take part in the discussion and contribute your ideas to the class, which is an important part of how the course works.
Attendance is checked using the Ed Discussion system. On the days it is taken, attendance acts as a registration (check-in). Not every class will have a check-in, but you should assume any class might.
You have three "free" absences, provided you email the teaching team at instructors-ccm-fall2026@gureckislab.org before the start of class. These cover the ordinary reasons people miss a class: being sick, transit problems, travel, and so on. You do not need to give a detailed explanation, but the email must arrive before class begins. If you are absent on a day attendance is taken and we do not have an email from you sent before class, it counts as an unexcused absence. Unexcused absences, and excused absences beyond the first three, reduce your attendance grade proportionally.
🎯 Final Project
The final project proposal is due Thursday, Oct 22 (0.5 pages written). Please submit via email to instructors-ccm-fall2026@gureckislab.org with the file name lastname1-lastname2-lastname3-ccm-proposal.pdf. Make sure to include the names of all of your group members at the top of the document too.
The final project will be done in groups of 3-4 students. A short paper will be turned in describing the project (max 6 pages). The project will represent either a substantial extension of one of the homeworks (e.g., exploring some new aspect of one of the assignments), implementing and extending an existing cognitive modeling paper, or a cognitive modeling project related to your research. We provide a list of project i.
The final project is due Thursday, December 17, 2026, 2:00PM-3:50PM. Please submit via email to instructors-ccm-fall2026@gureckislab.org with the file name lastname1-lastname2-lastname3-ccm-final.pdf. Make sure to include the names of all of your group members at the top of the document too and cc: all group members in your email.
The final project must relate to computational cognitive modeling and cannot be a purely machine learning / data science project. Thus, your project must connect, in some way, to the human mind and behavior. This could be collecting (informally) behavioral data to compare your computational model to. Or you could compare your model against results in the literature or particular dataset of human behavior or ratings. Or you could compare your algorithm with human intelligence in a more abstract sense. There are many ways to make the connection, but your final project must connect to cognitive modeling.
Your write-ups should be organized and written as a scientific paper. We require you to use this latex template. It must include the following sections: Introduction (with review of related work), Methods/Models, Results, and Discussion/Conclusion. Here is a good example:
Singh, P., Peterson, J. C., Battleday, R. M., & Griffiths, T. L. (2020). End-to-end deep prototype and exemplar models for predicting human behavior. Proceedings of the 42nd Annual Conference of the Cognitive Science Society. arXiv:2007.08723
Code submission is not required for the final project but can be linked in the write-up (e.g., to a public github).
Poster Presentation: During the final exam period (Thursday, December 17, 2026, 2:00PM-3:50PM in TBD), we will hold a mini poster conference where groups present their final projects. Each group will prepare a poster summarizing their project. Details on poster specifications and printing will be provided later in the semester.
📖 Readings
Each week may have an assigned reading which is linked directly from the lecture schedule. Look for the blue "Reading" pill box next to a lecture topic, which looks like this:
ReadingWhen a lecture has a Reading pill, you are expected to read that material before class. The readings provide important background for the lecture, and class time will build on them rather than repeat them. Not every week has an assigned reading — if there is no pill, there is nothing to prepare. Note that there may also be assigned readings for the lab sections (look for the same Reading pill on the lab schedule). Many of the readings are course notes written for this class, which are also always available under the "Course Notes" section of the sidebar on this website.
📜 Course policies and FAQ
🤝 Collaboration and honor code:
We take the collaboration policy and academic integrity very seriously. Violations of the policy will result in zero points and a possible disciplinary referral. You may discuss the homework assignments with your classmates, but you must run the simulations and complete the write-ups for the homework on your own. Under no circumstance should students look at each other’s code or write-ups, or code/write-ups from previous years of this course. Do not share your write-up or code with any of your classmates under any circumstances.
🤖 Generative AI:
You may use generative AI tools (ChatGPT, Claude, Copilot, and the like) on the homeworks and on the final project. You may not use them during the in-person exams, which are closed to all devices. Whatever tools you use, you are responsible for everything you turn in with your name on it. That means you have read it, you understand it, and you have checked that it is correct. If you submit something that is wrong, or code that does not do what your write-up says it does, "the AI wrote it" is not an explanation. Treat these tools the way you would treat advice from a classmate: useful, sometimes wrong, and your job to verify.
⏰ Late work:
We will take 10% off each day a homework or final project is late. Assignments should be turned in all at once and not in pieces. If an assignment is incomplete and later completed, the late penalty is applied to the entire assignment.
📩 Extensions:
If you are requesting an extension, email the teaching team (instructors-ccm-fall2026@gureckislab.org) and explain the reason. You must submit a request for an extension at least 24 hours before the due date of the assignment.
🔄 Regrading:
If you feel there was a mistake in the grading of your assignment, you can formally request a regrade by emailing the teaching team (instructors-ccm-fall2026@gureckislab.org). This will prompt us to regrade the full portion of the assignment and could lead to your grade being either raised or lowered depending on what the regrade finds.
🚫 Extra credit:
No extra credit will be given, out of interest of fairness.
☁️ Preconfigured cloud environment
Students registered for the course have the option of completing homework assignments on their personal computers, or in a cloud Jupyter environment with all required packages pre-installed. Students can log onto the environment using their nyu net ids at: https://dsga-1016-fall.rcnyu.org/.