Co-contraction embodies uncertainty: An optimal feedforward strategy for robust motor control.
Despite our environment often being uncertain, we generally manage to generate stable motor behaviors. While reactive control plays a major role in this achievement, proactive control is critical to cope with the substantial noise and delays that affect neuromusculoskeletal systems. In particular, m...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2024-11-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1012598 |
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| _version_ | 1850263534297939968 |
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| author | Bastien Berret Dorian Verdel Etienne Burdet Frédéric Jean |
| author_facet | Bastien Berret Dorian Verdel Etienne Burdet Frédéric Jean |
| author_sort | Bastien Berret |
| collection | DOAJ |
| description | Despite our environment often being uncertain, we generally manage to generate stable motor behaviors. While reactive control plays a major role in this achievement, proactive control is critical to cope with the substantial noise and delays that affect neuromusculoskeletal systems. In particular, muscle co-contraction is exploited to robustify feedforward motor commands against internal sensorimotor noise as was revealed by stochastic optimal open-loop control modeling. Here, we extend this framework to neuromusculoskeletal systems subjected to random disturbances originating from the environment. The analytical derivation and numerical simulations predict a characteristic relationship between the degree of uncertainty in the task at hand and the optimal level of anticipatory co-contraction. This prediction is confirmed through a single-joint pointing task experiment where an external torque is applied to the wrist near the end of the reaching movement with varying probabilities across blocks of trials. We conclude that uncertainty calls for impedance control via proactive muscle co-contraction to stabilize behaviors when reactive control is insufficient for task success. |
| format | Article |
| id | doaj-art-4df15d612ca640e092fdb79074630e09 |
| institution | OA Journals |
| issn | 1553-734X 1553-7358 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj-art-4df15d612ca640e092fdb79074630e092025-08-20T01:54:57ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582024-11-012011e101259810.1371/journal.pcbi.1012598Co-contraction embodies uncertainty: An optimal feedforward strategy for robust motor control.Bastien BerretDorian VerdelEtienne BurdetFrédéric JeanDespite our environment often being uncertain, we generally manage to generate stable motor behaviors. While reactive control plays a major role in this achievement, proactive control is critical to cope with the substantial noise and delays that affect neuromusculoskeletal systems. In particular, muscle co-contraction is exploited to robustify feedforward motor commands against internal sensorimotor noise as was revealed by stochastic optimal open-loop control modeling. Here, we extend this framework to neuromusculoskeletal systems subjected to random disturbances originating from the environment. The analytical derivation and numerical simulations predict a characteristic relationship between the degree of uncertainty in the task at hand and the optimal level of anticipatory co-contraction. This prediction is confirmed through a single-joint pointing task experiment where an external torque is applied to the wrist near the end of the reaching movement with varying probabilities across blocks of trials. We conclude that uncertainty calls for impedance control via proactive muscle co-contraction to stabilize behaviors when reactive control is insufficient for task success.https://doi.org/10.1371/journal.pcbi.1012598 |
| spellingShingle | Bastien Berret Dorian Verdel Etienne Burdet Frédéric Jean Co-contraction embodies uncertainty: An optimal feedforward strategy for robust motor control. PLoS Computational Biology |
| title | Co-contraction embodies uncertainty: An optimal feedforward strategy for robust motor control. |
| title_full | Co-contraction embodies uncertainty: An optimal feedforward strategy for robust motor control. |
| title_fullStr | Co-contraction embodies uncertainty: An optimal feedforward strategy for robust motor control. |
| title_full_unstemmed | Co-contraction embodies uncertainty: An optimal feedforward strategy for robust motor control. |
| title_short | Co-contraction embodies uncertainty: An optimal feedforward strategy for robust motor control. |
| title_sort | co contraction embodies uncertainty an optimal feedforward strategy for robust motor control |
| url | https://doi.org/10.1371/journal.pcbi.1012598 |
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