2021 Fiscal Year Research-status Report
Enhancing motor skill learning by manipulating extrinsic and intrinsic components of the motor task
| Project/Area Number |
21K17789
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| Research Institution | Tokyo Institute of Technology |
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Principal Investigator |
Barradas Victor 東京工業大学, 科学技術創成研究院, 特任助教 (70883908)
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| Project Period (FY) |
2021-04-01 – 2024-03-31
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| Keywords | speed of motor learning / manipulability ellipse / target distribution / muscle co-contraction |
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| Outline of Annual Research Achievements |
The main objective of this project is to identify intrinsic and extrinsic factors that can be exploited to increase the speed of learning a motor task. I have created a computational model that allowed me to identify three factors for a simple isometric arm reaching task. Two of these factors are extrinsic: the shapes of the controllability ellipsoid of the arm, and of the distribution of target forces in the task. One factor is intrinsic: the level of muscle co-contraction. In a series of experiments, I have confirmed that controlling these factors influences the speed of learning the task. These results validate the predictive ability of the model, and set the ground for using the model in more complex motor tasks. This is remarkable because the model might be useful for increasing the speed of learning in real-life motor tasks. Furthermore, the current work sets a significant precedent in using machine learning techniques to understand learning mechanisms in the brain, which recently has been a highly debated topic.
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| Current Status of Research Progress |
Current Status of Research Progress
2: Research has progressed on the whole more than it was originally planned.
Reason
The project is progressing smoothly according to the timeline that I had initially defined. That is, the phase of the project involving isometric tasks is almost complete, which includes the computational model, the respective experiments, and the writing of the results. However, publication of the results is taking longer than expected due to a lengthy peer review process.
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| Strategy for Future Research Activity |
As defined in the project timeline, the next phases of the project are 2) the kinematic phase, and 3) the tool phase. In the kinematic phase I will expand the computational model to more realistic tasks that involve actual movement. This involves computational modeling and experiments. I have already started work on this phase of the project and it will extend through 2022 and a few months in 2023. The tool phase involves exploiting the computational framework to design tools that are more easily learnable for a given task. I will work on this phase of the project during the remainder of 2023.
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| Causes of Carryover |
Because of delays in the publication cycle and the impossibility of travel due to the global pandemic, funds could not be used as intended. I will use the funds that are carried over to the next fiscal year the same way as I originally intended. That is, I will use the funds to pay fees for publication in peer-reviewed journals of papers that are already under review, and travel expenses to international conferences, if travel restrictions are lifted this year.
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