Motor behavior involves creating and executing appropriate action plans based on goals and relevant
information. This information characterizes the state of environment, the task and the state of
actions performed. The perceptual system gathers this information from different sources: touch,
vision, audition, scent and taste. Despite the richness of environment and the sophistication of our
sensory system, it is not possible to extract a complete and accurate representation of the required
states for motor behavior because of noise and ambiguity. Consequently, people effectively have
“limited information” and therefore may not be certain about the outcomes of specific actions. For
motor behavior to be robust to uncertainty, the brain needs to represent both relevant states and their
uncertainties, and it needs to build compensation for uncertainty into its motor strategy. Generating
motor behavior requires the brain to convert goals and information into action sequences, and the
flexibility of human motor behavior suggests that brain implements a complex control model. The
primary goal of this work is to improve the characterization of this control model by studying
motor compensation for uncertainty and determining the neural mechanisms underlying information
processing and the control model.
Part of this thesis focuses on studying human compensation strategies in natural tasks like grasping.
We experimentally tested the hypothesis that people compensate for object position uncertainty
by adopting strategies that minimize the impact of uncertainty in grasp success. As we hypothesized,
we found that people compensate for object position uncertainty by approaching the object
along the direction of maximal position uncertainty. Additionally, we modeled the grasping task
within the optimal control framework and found that human strategies share many characteristics
with optimal strategies for grasping objects with position uncertainty.
We are also interested to understand how the brain encodes and processes information relevant to
movements. To accomplish this, we studied the spatial and temporal interactions of cortical regions
underlying continuous and sequential movements using magnetoencephalography (MEG). Particularly,
we took data from a previous study, in which subjects continuously copied a pentagon shape
for 45 s using an XY joystick. Using Box-Jenkins time series analysis techniques, we found that
neural interactions and variability of movement direction are integrated in a feedforward-feedback
scheme. MEG sensors related to feedforward scheme were distributed around the left motor cortex
and the cerebellum, whereas sensors related to feedback scheme had a strong focus around the
parietal and the temporal cortices.
University of Minnesota Ph.D. dissertation. August 2010. Major: Computer Science. Advisors: Paul R. Schrater & Apostolos P. Georgopoulos. 1 computer file (PDF); xix, 142 pages, appendices A-B. Ill. (some col.)
Christopoulos, Vassilios N..
Characteristic information required for human motor control:Computational aspects and neural mechanisms..
Retrieved from the University of Minnesota Digital Conservancy,
Content distributed via the University of Minnesota's Digital Conservancy may be subject to additional license and use restrictions applied by the depositor.