Description

An efficient and high-intensive bootcamp on the use of computational models to understand and model the human mind.

MIT's introductory program on computational psychology to explore how to model the human mind. Students will gain foundational knowledge in reinforcement learning (RL) and Bayesian modeling, and applying the RL framework to practical problems such as psychiatric diagnosis. The course concludes with a project proposal competition, where students receive feedback from MIT mentors.

This course introduces psychological theories expressed in engineering terms, combined with hands-on machine learning practices. It is designed for students curious about how cognitive and psychological processes can be modeled computationally, and how such models can help us better understand ourselves and others. The course highlights the evolving nature of the field at the intersection of AI and psychology, presenting multiple perspectives rather than a single MIT viewpoint, as the field is still considered a research frontier.

Topics covered include: applying reinforcement learning to model human decision-making and diagnose mental disorders; using Bayesian models to simulate, predict, and analyze human behavior; employing mechanistic computational models to link neural activity with behavior and modulate brain responses; exploring the computational principles of agents as a believer with goals; and advancing clinical utility by applying computational psychiatry to improve diagnosis and treatment in mental health.

Listeners are welcome to join!

Time and Location

Mon Jan 27 - Fri Jan 31, 2025

Every day 10-1pm ET
MIT Room 45-102
New lectures & special guest speakers!