Computational Models of Cognition (PSYC 179)
This course will explore various computational models used for studying cognition. This includes models of attention, decision making, reward-based and reinforcement learning, and working memory. The course will focus on both descriptive and mechanistic models and how these models are used to explain behavior, interpret neural activity, and reveal underlying neural mechanisms. We will emphasize circuit-level models that can provide testable predictions for future experiments in humans and other animals. Overall, the purpose is to introduce students to a broad class of recent computational models of cognitive processes.
[Next offering: Winter 2019]
Experimental Design, Methodology, and Data Analysis Procedures (PSYC 10)
Any observations you make about the world is nothing but a random sample of what is there and to be able to interpret it correctly, you need to use statistical inference. Broadly, in this course you learn how to interpret quantitative observations (i.e. data) about almost anything in life.
The course covers basic issues in statistical analysis and experimental design and focuses on both descriptive and inferential statistics. Specific topics include correlation and regression, two-sample t-tests, and analysis of variance. The primary goal of the course is to understand basic statistical analyses, and to develop the skills necessary to analyze data in subsequent laboratory courses. After taking this course, you should be able to understand basic statistical analyses reported in professional journals.
[Next offering: Spring 2019]
Decision making (PSYC 50.02)
In our daily lives we are faced with many decisions: what to eat for lunch, whether to spend the next hour on Facebook or on homework, or what courses to take next quarter. Some of those decisions require gradual deliberation while others can be made quickly. Nevertheless, to make any decision we rely on external information and what outcomes we expect from those decisions. Decisions are easy to make if information is complete and the outcomes are certain. But how does the brain combine different sources of partial information to make decisions in the face of uncertain outcomes?
In this course we will examine decision making from both behavioral and neurobiological points of view. Specifically, we will learn about different methods used in psychology, economics, and neuroscience (e.g. operant conditioning, game theory, reinforcement learning, electrophysiology, neuroimaging) to study decision making at various levels, from cognitive processes to underpinning neural activity.
[Next offering: Fall 2018]
Neuroeconomics (PSYC 080.01)
Neuroeconomics is a new emerging field in which a combination of methods from neuroscience, psychology, and economics is used to better understand how we make decisions. In this seminar course, we learn about economic and psychological theories that are used to investigate and interpret neural activity and processes which underlie decision making. We also examine how recent neurobiological discoveries are used to refine decision theories and models developed in psychology and economics. During this course, not only will students read and discuss the most current research findings in neuroeconomics, but also learn to develop new ideas/hypotheses and design experiments to test those ideas.
[Next offering: Spring 2018]