Real-world interactions involve multiple brains engaging together. Traditional neural metrics consider individuals in isolation. We will quantify engagement via student-teacher brain synchrony.
Real-world human interactions involve multiple brains dynamically engaging together. However, traditional neural metrics consider individual people in isolation. This project focuses on developing a metric for quantifying and optimizing cognitive performance and engagement using simultaneous recording and integration of multiple streams of neural data from student-teacher pairs interacting with each other.
Our main objective is to develop a neural metric of student-teacher brain synchrony that relates to cognitive performance and engagement. Desired outcomes of the project are two peer reviewed publications (one methodological, one applied), and sufficient pilot data/proof of concept for submission of competitive proposals for external funding to both the National Science Foundation and the National Institutes of Health.
Our plan is to use the bulk of these funds towards salary support for a postdoctoral trainee in educational neuroscience. We also plan to collaboratively engage an undergraduate neuroscience student, a team of masters students in data science, and a doctoral student in quantitative psychology on this work.