200222 SE Theory and Empirical Research (Mind and Brain) 1 (2024S)

This course provides an introduction to the leading computational frameworks for understanding human behavior and cognition. Psychologists are increasingly confronted with large amounts of human behavioral data, and computational methods can help make sense of this data. The course focuses on practical and programming aspects, while also discussing the theoretical implications for psychology and cognitive science. To this end, the course will consist of conceptual lectures for the first 90 minutes, followed by programming labs for another 90 minutes every Wednesday.

The course will be divided into three parts. The first part will introduce the Python programming language and the most important libraries for data analysis, including NumPy, SciPy, Matplotlib, and pandas. The second part will focus on general data science methods, such as statistical inference with resampling methods, regression models, and machine learning. In the final section of the course, we will apply the programming skills we have acquired to model concepts that are particularly relevant to cognitive science, such as neural networks and reinforcement learning.

Weekly homework assignments will involve testing and implementing the techniques taught in the current and possibly previous weeks. The final project will require students to work in groups on a specific topic to analyze data, write a proposal, and present the project in class. By the end of the course, students will have a more comprehensive understanding of how computational methods advance psychology, how psychology can inform research in machine learning and AI, and how cognitive models are adapted and evaluated to understand behavioral data.