Control-Systems Approach to Modeling Human Control Strategies and Human Learning

Humans possess exceptional learning capabilities that help them control complex systems with virtually no a priori information. For example, humans ride bicycles, fly kites, and play with hula hoops. No existing control technique can match a human's ability to learn to interact with a wide variety of uncertain dynamic systems. Our research seeks to address questions of human learning and control: What control strategies do humans learn? How do humans learn to control unknown dynamic systems?

A predominant human-learning theory in neuroscience is the internal model hypothesis (IMH), which proposes that humans construct models of the physical world and use these models for control. The IMH has been explored using a variety of approaches. However, evidence in support of the IMH is inconclusive.
In this seminar, I will present results from a series of experiments in which human subjects learn to control unknown dynamic systems. A new subsystem identification method is used to estimate the controllers employed by each subject during each trial of the experiment. These identified controllers provide insight into the control strategies used by the subjects. For example, after a sufficient number of interactions with an unknown minimum-phase plant, the subjects' identified feedforward controllers approximate the inverse plant dynamics. This result supports the IMH.

Jesse Hoagg is an Assistant Professor of Mechanical Engineering at the University of Kentucky. His research interests include decentralized control, adaptive control, robust control, and system identification with applications to flight control, flow control, and structural control. Prior to joining the University of Kentucky, Dr. Hoagg was a postdoctoral research fellow at the University of Michigan and worked for the consulting firm McKinsey & Company. Dr. Hoagg received the Ph.D. degree in aerospace engineering from the University of Michigan in 2006. Dr. Hoagg also received the M.S.E. degree in aerospace engineering from the University of Michigan, the M.S. degree in mathematics from the University of Michigan, and the B.S.E. degree in civil and environmental engineering from Duke University.