Wilson, Spencer Ryan;
(2024)
Sensorimotor Learning in Virtual Environments.
Doctoral thesis (Ph.D), UCL (University College London).
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Abstract
The computational underpinnings of human motor learning remain an exciting frontier at the intersection of neuroscience, statistics, and engineering. This work sits at that intersection, exploring the computational principles and strategies employed by the brain during acquisition and refinement of a dexterous motor skill. We begin by reviewing relevant motor physiology research and illustrate how hallmarks of human motor skill learning are evident in the architecture of the motor system. We provide a breakdown of the experimental design, hardware, and software used in this work for the benefit of future colleagues. Our analysis begins with an overview of task performance data across subjects to confirm the integrity of our experimental design. We then explore the manifolds of subjects' electromyography data, providing a foundation for testable hypotheses about the evolution of muscle activations across learning. Next, we employ mixture models to extract statistics from our EMG data and refine hypotheses on the structure of subjects' variability. We then test hypotheses pertaining to the structure of task-relevant and task-irrelevant variability subspaces. We find that, over the course of learning, subjects identify and hone task-specific solutions with decreasing task-irrelevant variability, suggesting that subjects leverage a "model-free" learning strategy. We provide a simple reinforcement model for comparison to prior results. We conclude with a discussion of possible future directions for the research program developed here. We argue that high-dimensional EMG data offers a powerful platform to dissect our remarkably flexibly motor abilities. Finally, we issue a call-to-action towards deeper interdisciplinary collaborations bridging human motor experiments with computational analysis in an effort to reverse engineer the complexity of the intelligent movement machine.
| Type: | Thesis (Doctoral) |
|---|---|
| Qualification: | Ph.D |
| Title: | Sensorimotor Learning in Virtual Environments |
| Open access status: | An open access version is available from UCL Discovery |
| Language: | English |
| Additional information: | Copyright © The Author 2024. Original content in this thesis is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) Licence (https://creativecommons.org/licenses/by-nc/4.0/). Any third-party copyright material present remains the property of its respective owner(s) and is licensed under its existing terms. Access may initially be restricted at the author’s request. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > The Sainsbury Wellcome Centre |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10200089 |
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