I use EEG and TMS, as well as big data collection tools such as Amazon Mechanical Turk and computational modeling to investigate important questions about how learning, memory, and cognitive control intersect with our ability to plan and execute complex motor behaviors. Specifically, I am interested in the serial order problem (how we successfully plan and execute actions in the correct order), the underlying cognitive processes which allow us to understand another person’s actions (associative sequence learning, mirror neurons), motor imagery, and the role of the anterior cingulate and frontal cortex during skilled action sequencing. Ultimately, my goal is to learn more about the mechanical and cognitive processes involved in motor performance in order to improve machine learning algorithms that can be used to detect specific profiles of EEG activity and improve the performance of brain machine interfaces for people with mobility disabilities. Students who work in my lab can expect to develop coding and data collection/analysis skills that can be useful for careers in academia or the private and public sector. My research program is interdisciplinary, intersecting with computer science, cognitive psychology, and neuroscience, with distinct clinical and commercial applications. One of my recent publications was highlighted by the APA as an “Exciting Experiment in Psychology.”
