Optimality, Objectives, and Trade-Offs in Motor Control under Uncertainty

Ryu, Hansol

Optimality, Objectives, and Trade-Offs in Motor Control under Uncertainty

dc.contributor.advisor	Bertram, John E. A.
dc.contributor.author	Ryu, Hansol
dc.contributor.committeemember	Srinivasan, Manoj
dc.contributor.committeemember	Whelan, Patrick
dc.contributor.committeemember	Sternad, Dagmar
dc.contributor.committeemember	Cone, Jackson
dc.date	2023-11
dc.date.accessioned	2023-10-02T18:32:59Z
dc.date.available	2023-10-02T18:32:59Z
dc.date.issued	2023-09-22
dc.description.abstract	Biological motor control involves multiple objectives and constraints. In this thesis, I investigated the influence of uncertainty on biological sensorimotor control and decision-making, considering various objectives. In the first study, I used a simple biped walking model simulation to study the control of a rhythmic movement under uncertainty. Uncertainty necessitates a more sophisticated form of motor control involving internal model and sensing, and their effective integration. The optimality of the neural pattern generator incorporating sensory information was shown to be dependent on the relative amount of physical disturbance and sensor noise. When the controller was optimized for state estimation, other objectives of improved energy efficiency, reduced variability, and reduced number of falls were also satisfied. In the second study, human participants performed regression and classification tasks on visually presented scatterplot data. The tasks involved a trade-off between acting on small but prevalent errors and acting on big but scarce errors. We used inverse optimization to characterize the loss function used by humans in these regression and classification tasks, and found that these loss functions change systematically as the data sparsity changed. Despite being highly variable, there were overall shifts towards compensating for prevalent small errors more when the sparsity of the visual data decreased. In the third study, I extended the pattern recognition tasks to include visually mediated force tracking. When participants tracked force targets with visual noise, we observed a slight yet consistent force tracking bias. This bias, which increased with noise, was not explained by commonly hypothesized objectives such as a tendency to reduce effort while regulating error. Additional experiments revealed that a model balancing error reduction and transition reduction tendencies effectively explained and predicted experimental data. Transition reduction tendency was further separated into recency bias and central tendency bias. Notably, this bias disappeared when the task became purely visual, suggesting that such biases could be task-dependent. These findings across the three studies provide useful insights into understanding how uncertainty changes objectives and their trade-offs in biological motor control, and in turn, results in a different control strategy and behaviors.
dc.identifier.citation	Ryu, H. (2023). Optimality, objectives, and trade-offs in motor control under uncertainty (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.
dc.identifier.uri	https://hdl.handle.net/1880/117308
dc.language.iso	en
dc.publisher.faculty	Graduate Studies
dc.publisher.institution	University of Calgary
dc.rights	University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.
dc.subject	motor control
dc.subject	sensorimotor control
dc.subject	optimization
dc.subject	inverse optimization
dc.subject	perception
dc.subject	decision-making
dc.subject.classification	Engineering--Biomedical
dc.subject.classification	Neuroscience
dc.subject.classification	Engineering--Mechanical
dc.title	Optimality, Objectives, and Trade-Offs in Motor Control under Uncertainty
dc.type	doctoral thesis
thesis.degree.discipline	Engineering – Biomedical
thesis.degree.grantor	University of Calgary
thesis.degree.name	Doctor of Philosophy (PhD)
ucalgary.thesis.accesssetbystudent	I do not require a thesis withhold – my thesis will have open access and can be viewed and downloaded publicly as soon as possible.