Computational cognitive science - Spring 2026
Instructor: Todd Gureckis and Mark Ho
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.
Course numbers:
PSYCH-UA 300-011 (Psychology)
Summary: Computational cognitive science aspires to understand the mind and brain by building computational models of the hidden cognitive processes that give rise to observable behavior. 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, functioanlly 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 the homeworks (65%) and the final project (35%).
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.