Characteristic information required for human motor control:Computational aspects and neural mechanisms.

Abstract

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.