Quantitative Methods in Cognitive Science: Data Analysis, Modeling, and Visualization

Catalog description

This seminar introduces students to common quantitative methods used to study cognitive processes based on behavioral measurements. The course covers experimental design, data analysis and visualization, and an introduction to basic models of cognitive behavior. Students will get to know common experimental designs to test cognitive behavior, how to perform hypothesis testing, and apply and fit theoretical models such as signal detection theory, ideal observer models, and drift-diffusion models. Throughout the course, students will independently analyze cognitive behavioral datasets, culminating in a research paper that showcases their ability to use the learned methods for an unbiased and effective analysis and interpretation of cognitive processes, and to visualize and present their results. A key emphasis of the course is on scientific rigor: students will learn to avoid common mistakes in analyzing, interpreting, and presenting data that have substantially harmed the reputation of cognitive and psychological sciences within the scientific community as well as the general public (e.g. the “reproduction crisis”).