Computational and Formal Modeling
Lectures, practical sessions, discussion and a modeling project.
This course is compulsory for third year students in the degree program Bachelor of Artificial Intelligence students. It is assumed that students have introductory-level knowledge of Artificial Intelligence, Cognitive Psychology, Formal Thinking, Programming, and The Philosophy of Cognitive Science. The course is also suitable for students who are doing a BSc, MSc and PhD in Psychology or Cognitive Neuroscience and who want to learn about computational and formal modeling.
The student will:
- Learn how to formalise informal verbal cognitive theories.
- Practice implementing and simulating computational cognitive models.
- Be able to think critically about existing computational cognitive models, their properties, and their relationships.
- Learn about some of the methodological problems and challenges in cognitive modeling.
- Learn how to relate cognitive modeling issues to the broader context of Cognitive Neuroscience, Artificial Intelligence, Psychology and Philosophy.
- Practice his/her English verbal and writing skills.
- Be able to read and think critically about original literature on cognitive modeling.
Cognitive scientists build computational models of mental processes in order to achieve understanding of how the mind/brain works. Such models try to capture in a precise (formal) language our (informal) intuitions about how aspects of human cognition may work. By systematically studying our models and comparing them to actual human cognitive behaviour, we can test our ideas about how the mind works.
The aim of this course is to introduce the student to the conceptual foundations and contemporary practice of computational modeling in Cognitive Science research. Students will learn to become critical users and designers of computational models. During supervised assignments, the student will gain hands-on experience in formalising verbal theories and with analysing and evaluating a variety of computational models (including symbolic, connectionist, dynamical, and probabilistic models). During the final modeling project, each student will have the opportunity to practice improving upon existing models and/or developing an entirely new model of a cognitive phenomenon which has captured his/her interest.
Type of Exam(s):
- Take-home exam
- Selected readings (original articles)
- Course material