Introduction to Cognitive Modeling

CHI 2025 Onsite Course, April 30

Course highlights

Learn to understand, create, and utilize computational cognitive models in HCI contexts.
Use Python notebooks to easily simulate interactive task behaviour in various environments.
Topics include computational rationality, deep reinforcement learning, model design

This course introduces computational cognitive modeling for researchers and practitioners in the field of HCI. Cognitive models use computer programs to model how users perceive, think, and act in human-computer interaction. They offer a powerful approach for understanding interactive tasks and improving user interfaces. This course starts with a review of classic approaches to cognitive modeling. It then rapidly progresses to introducing computational rationality, which uses modern modeling approaches powered by machine learning methods, in particular deep reinforcement learning. The course is built around hands-on Python programming using notebooks.

Course contents

The course consists of three modules.

Introduction to cognitive modeling. In this session we will critically review the history of cognitive modeling in HCI over the past 50 years. The review will cover modeling approaches that include mathematical models of movement time, procedural models of skill, models of eye movements, computational simulations of human information processing, models inspired by how animals forage for food and models of human learning, rationality and decision making. After completing the section the attendees will understand the role of computational cognitive modeling in HCI, and know how the historical approaches have converged into the state-of-the-art of modeling.
Computational rationality. In this unit, we cover how to create computational rational models of interaction. The session provides an introduction to creating deep reinforcement learning based simulation models of interac- tive tasks. We cover the method of defining goals, bounds, and the interaction environment, and how to simulate such task environ- ments to make realistic predictions of complex interaction. After completing this section, attendees understand what the principles of computational rational models of interaction are, and how to derive bounded optimal interactive policies using deep RL.
Building a model for HCI. In the final unit, we apply what was learned in the previous units and design and build a model of interactive task behavior from starting principles of computational rationality. This module cov- ers the steps required for making computational rational agents, starting from defining the internal and external task environments and the agent’s goals. We also cover how to train deep RL agents and how to inspect their predictions. The focus is on investigating how the design of the task environment and the internal cogni- tive processes interact in a way that is consistent with empirically observed human-like behavior.

Course materials are available at https://github.com/jussippjokinen/CogMod-Tutorial