Computational cognitive science - Spring 2026

Instructor: Todd Gureckis and Mark Ho

Teaching Assistants: Maya Malaviya

Course email: instructors-ccs-spring2026@gureckislab.org (use this for general inquiries)

Meeting time and location: Monday and Wednesday, 9:30am-10:45am in GCASL 361.

Office hours: Default is in person, but email us to request zoom if need be.

• Todd Gureckis: Tuesdays 2-3pm in Meyer 590
• Mark Ho: Thursdays 2:30pm-4pm by appointment (Sign up here) in Meyer 552
• Maya Malaviya: Wednesday 2-4pm or by appointment (Sign up here) in Meyer 571

Course numbers:
PSYCH-UA 300-011 (Psychology)

Summary: Computational cognitive science aims to understand the mind and brain by building computational models of the cognitive processes that produce the data. This course introduces the goals, philosophy, and technical concepts behind computational cognitive science.

Key questions include: How is human intelligence diffferent from artificial intelligence? How can perception, memory, learning, and social interaction be understood as computational processes? How are symbols important for intelligence? How does the mind simulate reality?

The lectures cover modeling techniques including artificial neural networks (deep learning), reinforcement learning, Bayesian modeling, model comparison and fitting, classification, probabilistic graphical models, and program induction. The psychological applications span a broad set of psychological abilities including learning, categorization, language, memory, decision-making, and social interaction. 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.

Pre-requisites

The course is designed for advanced students in psychology, data science, or computer science who are interested in understanding human and machine intelligence. There are no required prerequisites for the course. However, functionally prerequisites are a basic familiarity with programming (we will use Python in the course problem sets) and comfort with talking about ideas from elementary calculus, linear algebra, and probability theory.

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. More information can be found here.
• Python programming homeworks (15%): Four homework assignments.
• Final project (35%): See details below.

Class participation may be used to decide grades in borderline cases.

Final Project

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.

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 policy: The use of generative AI for the written portions of the course is prohibited. The goal of this course is to learn about the field and material, and generative AI is not a substitute for that. If we suspect that generative AI is used in your work, we will likely create alternative assessments based on in person discussion of the material.

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-ccs-spring2026@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 through Gradescope. 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. You can submit a regrade request via Gradescope.

Did you forget to turn in part of the homework, or did it print improperly on the PDF?: We will not regrade homework because your answer did not display correctly in the version you submitted. Before turning in your assignment, you must double-check that all of your answers appear clearly in the PDF printout.

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://psychua300-011-spring.rcnyu.org/.

Disability Disclosure Statement

Academic accommodations are available for students with disabilities. The Moses Center website is www.nyu.edu/csd. Please contact the Moses Center for Students with Disabilities (212-998-4980 or mosescsd@nyu.edu) for further information. Students who are requesting academic accommodations are advised to reach out to the Moses Center as early as possible in the semester for assistance.