This research explores how consumers make decisions when faced with uncertainty, limited information, or complex choices. The USOAR student will help design and analyze large-scale online experiments to uncover how memory, beliefs, and metacognition influence consumer behavior.
Why do people choose certain products over others? How do they decide when they don’t have all the information? This project investigates how consumers make decisions in real-world contexts—like online shopping, service selection, or media use—using carefully designed online experiments. By studying how memory, knowledge, and other psychological forces influence consumer choices, we aim to uncover the hidden cognitive processes that shape decision-making in everyday life.
As part of a larger research program that bridges cognitive science and marketing, this work is grounded in interdisciplinary insights from neuroscience, psychology, and behavioral economics. The experiments you’ll help design and analyze will not only contribute to fundamental questions about how people think and choose, but also have implications for marketing strategy, policy design, and consumer welfare.
The broader goal is to better understand how memory and beliefs interact with information environments to shape behavior. You will be directly involved in the research process—from brainstorming hypotheses to analyzing behavioral patterns in data collected from thousands of participants.
This is an ideal opportunity for students interested in experimental research, consumer behavior, or applied cognitive science. No prior experience is required, but curiosity, reliability, and a willingness to learn are a must.
- Basic knowledge of statistics
- Interest in psychology, marketing, behavioral science, or related fields
- Prior experience with Python, R, or Qualtrics is a plus
- How to design and run large-scale behavioral experiments using online platforms (e.g., Qualtrics, Prolific)
- Basics of consumer decision-making theories and experimental psychology
- Data cleaning and visualization in Python or R
- How to formulate and test hypotheses using statistical methods