Research

The effortless ease with which we move masks a profound complexity. While we can now build a car that can drive autonomously for thousands of miles and rival the performance of a human driver, we still cannot build a robot that can get into that car with the grace of even a 6-year old child. 

Nearly every movement a person makes represents a decision, where the individual must weigh the costs and rewards of each movement and use this as a basis upon which they choose among competing movements and learn to make new ones. Our goal is to gain fundamental knowledge about the subjective costs and rewards underlying movement decision making and to apply this understanding towards the development of rehabilitation therapies for individuals with movement disorders such as Parkinson鈥檚 Disease. We use a novel neuroeconomic approach that combines concepts and tools from engineering, neuroscience, psychology, and economics to better understand why we move in the ways we do and how we learn to move in new ones. This framework of movement neuroeconomics is advantageous because it allows for the quantification of the subjective costs and rewards and the identification of the neural mechanisms underlying movement decision making. Moreover, it provides us with a common language with which to compare decision making across populations and decision-making domains (i.e., movement, economic and social decision making). 

Experiments use a combination of approaches involving virtual reality, robotic interfaces, kinetic and kinematic analyses. Coupled with computational models, these investigations will provide greater insight into the interplay between the biomechanical and sensorimotor processes underlying human movement control and decision-making. Projects in the lab investigate:

  1. The role of effort and reward in decision making and movement control
  2. Risk-sensitivity in movement control and adaptation
  3. The role of metabolic cost in motor learning
  4. Effect of age and coactivation on movement control
  5. Motor control and learning in Parkinson's Disease and Multiple Sclerosis
  6. Dynamics learning and generalization for postural control
  7. Using the Wii Balance Board for teaching biomechanics
  8. Optimal Foraging in movements