Associate Professor, Physical Medicine and Rehabilitation/Physiology, Northwestern University
The research of the Bayesian Behavior group shows that movement and movement learning can be understood in terms of statistical principles. Our Sensors (Eyes, Ears, Skin etc) are not perfect but are noisy. Moreover, our muscles are noisy and if we try to do the same movement over and over it will be different each time. This means that if we make a movement, say swing a golf club, we will have uncertainty in the potential movement outcomes. Our group studies how people make movement decisions in the presence of such uncertainty.
Our research has four main thrusts:
- We advance big Data approaches to neuroscience
- We study experimentally how people move and how their movements are affected by uncertainty.
- We build computational models using Bayesian statistics to calculate how people could move optimally or learn to move optimally.
- We build Bayesian Algorithms to solve problems that we find interesting. For example we analyze how neurons are connected in the nervous system.
The main thrust of our current research is to allow for better rehabilitation procedures through an understanding of motor learning.
Our lab is part of Northwestern University, Departments Physiology and PM&R. It is associated with Northwestern Department of applied math. Our laboratory is part of the rehabilitation institute of Chicago.