Bayes'd and confused: novel applications of Bayesian inference to better understand sensorimotor uncertainty
Date
2021
Authors
Whittier, Tyler Thorley, author
Fling, Brett W., advisor
Rhea, Christopher K., committee member
Seidler, Rachael D., committee member
Weller, Zachary D., committee member
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Abstract
Effective motor control relies on accurate sensory information. However, sensory information is inherently variable and clouded with uncertainty. Yet, humans perform motor skills with a high degree of proficiency and reliability. How the central nervous system (CNS) controls motor function amid the uncertainty of sensory signals is not known. Researchers in recent years have suggested that the brain may control movement in a way that can be explained by Bayesian inference. Bayesian inference posits that the most probable outcome is the product of both the currently available data (sensory information) as well as previously collected data (learned expectations). Applying Bayesian inference to a motor control context, we suggest that the CNS accounts for the uncertainty in sensory information by filling in the gaps of uncertainty with learned expectations when forming beliefs on where our body parts are in space. While initial findings on this topic are promising, they predominantly involve one-dimensional upper-body tasks. The purpose of this dissertation was to determine if Bayesian model of sensorimotor control is consistent in a full body stepping movement and if it can be further utilized to understand sensory function in various contexts. The first study in this dissertation was done to discover if the center of mass (CoM) position is estimated in a Bayesian way during stepping, like what has been shown in upper body movements. The second study sought to identify if Bayesian position estimations are beneficial to overall motor performance. In the third study, we applied what we have discovered about Bayesian inference in full body movements to understand the effects of transcutaneous electric nerve stimulation (TENS) on positional awareness during motor control. We hope to build on these findings to better understand how sensory information is utilized by the CNS to control movement.
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Subject
motor control
proprioception
motor learning
Bayesian