In the Norman lab, we use computational models to explore how the brain gives rise to learning and memory phenomena, and we test these models’ predictions using neuroimaging studies where we decode people’s thoughts as they learn and remember. Currently, students in the lab are investigating questions like: What are the “learning rules” that govern how memories are modified in the brain? How does sleep contribute to learning? How are memories time stamped? How can we intentionally forget memories?
In our neuroimaging work, we are developing (along with other Princeton researchers) new machine learning methods for analyzing distributed patterns of neural activity. For example, we are developing “data mining” algorithms that can isolate the fMRI and EEG signatures of specific thoughts and memories; we can use these new analysis tools to track how people’s thoughts come and go and change over the course of an experiment. We are also developing real-time neurofeedback methods that allow us to adapt our studies on-line based on what people are thinking.