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Computational cognitive modeling

NYU PSYCH-GA 3405.002 / DS-GS 3001.006

Instructors: Brenden Lake and Todd Gureckis

Teaching Assistant: Alex Rich

Meeting time and location:
Tuesdays 6:45 PM - 8:25 PM (lecture)
Tuesdays 8:25 PM - 9:25 (lab)
NOTE ROOM CHANGE: Tisch Hall, 40 West 4th Street, Room LC9 (in Stern business school, lower concourse)

Course numbers:
PSYCH-GA 3405.002 (Psychology)
DS-GA 3001.006 (Data Science)

Contact information and Piazza:
We will be using Piazza for questions and class discussion. The system is catered to getting you help fast and efficiently from classmates, the TA, and the instructors. Rather than emailing questions to the teaching staff, we encourage you to 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 (Wednesdays 1-2 pm, or by appointment; 6 Washington Place, Meyer, Room 859)  

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

Alex Rich (Tuesday 12:30-1:30 pm, or by appointment; 6 Washington Place, Meyer, Room 856)

Key questions:

Summary: This course provides a survey of computational approaches to understanding human intelligence and cognition. Both psychologists and data scientists are working with increasingly large quantities of human behavioral data. Computational cognitive modeling is the project of understanding behavioral data (and the mind and brain, more generally) by building computational models of the cognitive processes that produce the data. We will cover the goals, philosophy, and technical concepts behind computational cognitive modeling, including model fitting and evaluation. Readings and lectures will survey various computational approaches, including artificial neural networks / deep learning, Bayesian / structured probabilistic models, symbolic and logical systems, and reinforcement learning. We will cover a broad set of psychological applications of these modeling approaches, including learning, memory, decision making, language, categorization, reasoning, and problem solving. Homework assignments will include implementing some of the modeling ideas surveyed in class. Ideally, 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 artificial intelligence, and how to fit and evaluate cognitive models for understanding 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 is due on Tuesday 5/15.

Final project will be done in groups of 2-4 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 written paper discussing one of the core modeling topics. The final projects will need to be approved by the instructor at least 6 weeks before the end of the semester.

Most write-ups should follow the structure of a standard scientific paper. It should 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:

Please submit your final prorject via email to with the file name lastname1-lastname2-lastname3-ccm-final.pdf


Detailed schedule and readings

Please see below for the assigned readings for each class. We strongly suggest reading the papers before the assigned class. Papers are available for download on NYU Classes in the “Resources” folder.

1/23 Introduction/Overview - Welcome, Course Policies, General Overview, Levels of analysis (slides)

1/30 Classification and category learning (slides)

2/6 Neural networks / Deep learning (part 1) (slides)

2/13 Neural networks / Deep learning (part 2) (slides)

2/20 Neural networks / Deep learning (part 3) (slides)

2/27 Reinforcement learning and decision making (part 1) (slides)

3/6 Reinforcement learning and decision making (part 2) (slides)

3/20 Reinforcement learning and decision making (part 3) (slides)

3/27 Bayesian modeling (part 1) (slides)

4/3 Bayesian modeling (part 2) (see link above)

4/10 Rational versus mechanistic modeling approaches (slides)

4/17 Model comparison and fitting, tricks of trade (slides)

4/24 Probabilistic graphical models(slides)

5/1 Program induction and language of thought models(slides)

Course policies

Unfortunately we have no additional spots for auditors due to the large number of previous requests. If we have replied to your request, you may audit pending available seats. Priority goes to registered students and then by date of audit request.

We encourage you to discuss the homework assignments with your classmates. We expect you to run the simulations and complete the write-ups for the homeworks on your own. Under no circumstance should students look at eachother’s write ups.

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

Extra credit:
In the interest of fairness no extra credit will be given.

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.