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Department of Health and Exercise Science

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https://hdl.handle.net/10217/100447
These digital collections include theses, dissertations, and faculty publications from the Department of Health and Exercise Science. Due to departmental name changes, materials from the following historical department is also included here: Physical Education.

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Browsing Department of Health and Exercise Science by Subject "Bayesian"

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    Bayes'd and confused: novel applications of Bayesian inference to better understand sensorimotor uncertainty
    (Colorado State University. Libraries, 2021) Whittier, Tyler Thorley, author; Fling, Brett W., advisor; Rhea, Christopher K., committee member; Seidler, Rachael D., committee member; Weller, Zachary D., committee member
    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.