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Computational cognitive modeling

NYU PSYCH-GA 3405.002 / DS-GS 3001.005

Instructors: Brenden Lake and Todd Gureckis

Teaching Assistants: Reuben Feinman and Anselm Rothe

Meeting time and location:
Mondays 1:50-3:30 PM
*ROOM CHANGE*: Silver Center for Arts & Science, 100 Washington Sq East, Room 520

Tuesdays 2:40-3:30 PM
60 Fifth Ave. Room 110

Course numbers:
DS-GA 3001.005 (Data Science)
PSYCH-GA 3405.002 (Psychology)

Contact information and Piazza:
We use Piazza for questions and class discussion. Piazza gets you help efficiently from classmates, the TA, and the instructors. Rather than emailing questions to the teaching staff, please post your questions on Piazza.

The signup link for our Piazza page is available here (

Once signed up, our class Piazza page is available here (

If there is a need to email the teaching staff directly, please use the following email address:

Office hours:
Todd Gureckis (Thursdays 2-4pm; 6 Washington Place, Meyer, Room 859)  

Brenden Lake (Wednesdays 10-11:00 am, or by appointment; 60 5th Ave., Room 610)

Reuben Feinman (Wednesdays 3-4pm; 60 5th Ave., Room 609)

Anselm Rothe (Fridays 2:30-3:30pm; 6 Washington Place, Meyer, Room 852) (Exception: In the week of Feb 21, Anselm’s office hour is on Thursday, Feb 21, 2:30-3:30pm in Room 856.)

Summary: 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. This course introduces the goals, philosophy, and technical concepts behind computational cognitive modeling.

The lectures cover artificial neural networks (deep learning), reinforcement learning, Bayesian modeling, model comparison and fitting, classification, probabilistic graphical models, and program induction. Modeling examples span a broad set of psychological abilities including learning, categorization, language, memory, decision making, and reasoning. The homework assignments include examining and implementing the models surveyed in class. Students will 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 to understand behavioral data.

Please note that this syllabus is not final and there may be further adjustments.



The final grade is based on homeworks (50%), final project (35%), and attendance/participation (15%).

Final Project

The final project proposal is due on Monday, April 1 (one half page written). Please submit via email to with the file name lastname1-lastname2-lastname3-ccm-proposal.pdf.

The final project is due on Tuesday, May 14. Please submit via email to with the file name lastname1-lastname2-lastname3-ccm-final.pdf.

The final project will be done in groups of 1-4 students. A short paper will be turned in describing the project (approximately 6 pages). The project will represent either an 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. Possible project ideas are listed here, but of course you do not have to choose from this list (it is just some examples).

Write-ups should be organized and written as a scientific paper. It must include the following sections: Introduction (with review of related work), Methods/Models, Results, and Discussion/Conclusion. A good example would be to follow the structure of this paper from the class readings:

Lecture schedule

Mondays 1:50-3:25 PM
60 Fifth Ave. Room 110

Lab schedule

Tuesdays 2:40-3:30 PM
60 Fifth Ave. Room 110

Readings and slides

Papers are available for download on NYU Classes in the “Resources” folder.

Neural networks and deep learning

Reinforcement learning and decision making

Bayesian modeling

Rational versus mechanistic modeling approaches

Model comparison and fitting, tricks of trade

Probabilistic graphical models

Program induction and language of thought models

Course policies

Please email instructor to see if there are available seats. Priority goes to registered students and then by date of audit request.

Collaboration and honor code:
We encourage you to discuss the homework assignments with your classmates. You must run the simulations and complete the write-ups for the homeworks on your own. Under no circumstance should students look at each other’s write ups or code, or write-ups or code from previous years.

Late work:
We will take off 10% for each day a homework or final project is late.

Extra credit:
No extra credit will be given, out of interest of fairness.

Laptops in class:
Laptops in class are discouraged. We know many try to take notes on their laptops, but it’s easy to get distracted (social media, etc.). This can also distract 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.

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 github login information here assuming they have contacted the TAs and provided their username.