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Bayes'd and confused: novel applications of Bayesian inference to better understand sensorimotor uncertainty

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dc.contributor.authorWhittier, Tyler Thorley, author
dc.contributor.authorFling, Brett W., advisor
dc.contributor.authorRhea, Christopher K., committee member
dc.contributor.authorSeidler, Rachael D., committee member
dc.contributor.authorWeller, Zachary D., committee member
dc.date.accessioned2022-01-07T11:31:05Z
dc.date.available2023-01-06T11:31:05Z
dc.date.issued2021
dc.description.abstractEffective 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.
dc.format.mediumborn digital
dc.format.mediumdoctoral dissertations
dc.identifierWhittier_colostate_0053A_16948.pdf
dc.identifier.urihttps://hdl.handle.net/10217/234308
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartof2020-
dc.rightsCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.
dc.subjectmotor control
dc.subjectproprioception
dc.subjectmotor learning
dc.subjectBayesian
dc.titleBayes'd and confused: novel applications of Bayesian inference to better understand sensorimotor uncertainty
dc.typeText
dcterms.embargo.expires2023-01-06
dcterms.embargo.terms2023-01-06
dcterms.rights.dplaThis Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
thesis.degree.disciplineHealth and Exercise Science
thesis.degree.grantorColorado State University
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy (Ph.D.)

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