Probability theory captures a number of essential characteristics of human cognition, including aspects of perception, reasoning, belief revision, and learning. Expressions of degree of belief were used in language long before people began codifying the laws of probability theory. This course explores the history and debates over codifying the laws of probability, how probability theory applies to specific cognitive processes, how it relates to the human understanding of causality, and how new computational approaches to causal modeling provide a framework for understanding human probabilistic reasoning.
This class is suitable for advanced undergraduates or graduate students specializing in cognitive science, artificial intelligence, and related fields. A course in cognitive science, and a course in probability or statistics, are helpful.
Class meets for two hours per week. Students are expected to do weekly readings in preparation for discussions during class.
Over the course of the semester, students will be required to produce an annotated bibliography based on the course readings: for each reading, students must write a short (approximately one paragraph) summary of the important points of the reading, and how that reading fits into the themes of the class.
Students will also be required to design, execute, and report on an original research experiment exploring how human behavior can be modeled using probability theory and/or causal structures.