This course will explore some of the most popular computational frameworks employed for understanding human intelligence and cognition. Psychologists, among other data scientists, have to deal with increasingly large and multidimensional datasets of human behavior. Computational cognitive modeling aims to understand this type of complex behavioral data, by building mathematical descriptions of the latent (hidden) cognitive processes that produce the observed data. Over the past decade, computational cognitive models have become instrumental in cognitive science to understand and even predict human behavior using mathematical descriptions of the mind. The main goal of this course is to provide students with the objectives, philosophy, and technical underpinnings of computational cognitive modeling.

Throughout lectures, students will delve into various subjects, including signal detection theory, reinforcement learning, Bayesian modeling, model fitting, model comparison, and artificial neural networks. The scope of modeling examples encompasses a wide array of psychological abilities such as categorization, learning, memory, decision-making, and reasoning. By the end of the course, students will possess a deeper understanding of how computational modeling can move cognitive science forward when it is applied adequately to address a particular research question. Students will also acquire the skills to fit, evaluate and compare computational cognitive models for a deeper understanding of complex multidimensional behavioral data.

Prerequisites:
basic/initial experience with math (linear algebra, probabilities, statistics)
basic/initial experience with coding (in whatever programming language)

ECTS: 6