[2404.13258] Human Motor Learning Dynamics in High-dimensional Tasks Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Electrical Engineering and Systems Science > Systems and Control arXiv:2404.13258 (eess) [Submitted on 20 Apr 2024] Title: Human Motor Learning Dynamics in High-dimensional Tasks Authors: Ankur Kamboj , Rajiv Ranganathan , Xiaobo Tan , Vaibhav Srivastava View a PDF of the paper titled Human Motor Learning Dynamics in High-dimensional Tasks, by Ankur Kamboj and 3 other authors View PDF HTML (experimental) Abstract: Conventional approaches to enhancing movement coordination, such as providing instructions and visual feedback, are often inadequate in complex motor tasks with multiple degrees of freedom (DoFs). To effectively address coordination deficits in such complex motor systems, it becomes imperative to develop interventions grounded in a model of human motor learning; however, modeling such learning processes is challenging due to the large DoFs. In this paper, we present a computational motor learning model that leverages the concept of motor synergies to extract low-dimensional learning representations in the high-dimensional motor space and the internal model theory of motor control to capture both fast and slow motor learning processes. We establish the model's convergence properties and validate it using data from a target capture game played by human participants. We study the influence of model parameters on several motor learning trade-offs such as speed-accuracy, exploration-exploitation, satisficing, and flexibility-performance, and show that the human motor learning system tunes these parameters to optimize learning and various output performance metrics. Comments: 22 pages (single column), 9 figures Subjects: Systems and Control (eess.SY) ; Dynamical Systems (math.DS) Cite as: arXiv:2404.13258 [eess.SY] (or arXiv:2404.13258v1 [eess.SY] for this version) https://doi.org/10.48550/arXiv.2404.13258 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Ankur Kamboj [ view email ] [v1] Sat, 20 Apr 2024 04:01:57 UTC (3,851 KB) Full-text links: Access Paper: View a PDF of the paper titled Human Motor Learning Dynamics in High-dimensional Tasks, by Ankur Kamboj and 3 other authors View PDF HTML (experimental) TeX Source view license Current browse context: eess.SY < prev | next > new | recent | 2024-04 Change to browse by: cs cs.SY eess math math.DS References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from