The dynamics of motor learning through the formation of internal models.
A medical student learning to perform a laparoscopic procedure or a recently paralyzed user of a powered wheelchair must learn to operate machinery via interfaces that translate their actions into commands for an external device. Since the user's actions are selected from a number of alternativ...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2019-12-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1007118&type=printable |
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| author | Camilla Pierella Maura Casadio Ferdinando A Mussa-Ivaldi Sara A Solla |
| author_facet | Camilla Pierella Maura Casadio Ferdinando A Mussa-Ivaldi Sara A Solla |
| author_sort | Camilla Pierella |
| collection | DOAJ |
| description | A medical student learning to perform a laparoscopic procedure or a recently paralyzed user of a powered wheelchair must learn to operate machinery via interfaces that translate their actions into commands for an external device. Since the user's actions are selected from a number of alternatives that would result in the same effect in the control space of the external device, learning to use such interfaces involves dealing with redundancy. Subjects need to learn an externally chosen many-to-one map that transforms their actions into device commands. Mathematically, we describe this type of learning as a deterministic dynamical process, whose state is the evolving forward and inverse internal models of the interface. The forward model predicts the outcomes of actions, while the inverse model generates actions designed to attain desired outcomes. Both the mathematical analysis of the proposed model of learning dynamics and the learning performance observed in a group of subjects demonstrate a first-order exponential convergence of the learning process toward a particular state that depends only on the initial state of the inverse and forward models and on the sequence of targets supplied to the users. Noise is not only present but necessary for the convergence of learning through the minimization of the difference between actual and predicted outcomes. |
| format | Article |
| id | doaj-art-6474aa6fb7494617878677fcbd35d7a1 |
| institution | OA Journals |
| issn | 1553-734X 1553-7358 |
| language | English |
| publishDate | 2019-12-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj-art-6474aa6fb7494617878677fcbd35d7a12025-08-20T02:17:05ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582019-12-011512e100711810.1371/journal.pcbi.1007118The dynamics of motor learning through the formation of internal models.Camilla PierellaMaura CasadioFerdinando A Mussa-IvaldiSara A SollaA medical student learning to perform a laparoscopic procedure or a recently paralyzed user of a powered wheelchair must learn to operate machinery via interfaces that translate their actions into commands for an external device. Since the user's actions are selected from a number of alternatives that would result in the same effect in the control space of the external device, learning to use such interfaces involves dealing with redundancy. Subjects need to learn an externally chosen many-to-one map that transforms their actions into device commands. Mathematically, we describe this type of learning as a deterministic dynamical process, whose state is the evolving forward and inverse internal models of the interface. The forward model predicts the outcomes of actions, while the inverse model generates actions designed to attain desired outcomes. Both the mathematical analysis of the proposed model of learning dynamics and the learning performance observed in a group of subjects demonstrate a first-order exponential convergence of the learning process toward a particular state that depends only on the initial state of the inverse and forward models and on the sequence of targets supplied to the users. Noise is not only present but necessary for the convergence of learning through the minimization of the difference between actual and predicted outcomes.https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1007118&type=printable |
| spellingShingle | Camilla Pierella Maura Casadio Ferdinando A Mussa-Ivaldi Sara A Solla The dynamics of motor learning through the formation of internal models. PLoS Computational Biology |
| title | The dynamics of motor learning through the formation of internal models. |
| title_full | The dynamics of motor learning through the formation of internal models. |
| title_fullStr | The dynamics of motor learning through the formation of internal models. |
| title_full_unstemmed | The dynamics of motor learning through the formation of internal models. |
| title_short | The dynamics of motor learning through the formation of internal models. |
| title_sort | dynamics of motor learning through the formation of internal models |
| url | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1007118&type=printable |
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