Bayesian integration and non-linear feedback control in a full-body motor task.
A large number of experiments have asked to what degree human reaching movements can be understood as being close to optimal in a statistical sense. However, little is known about whether these principles are relevant for other classes of movements. Here we analyzed movement in a task that is simila...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2009-12-01
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| Series: | PLoS Computational Biology |
| Online Access: | http://europepmc.org/articles/PMC2789327?pdf=render |
| _version_ | 1818133873700110336 |
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| author | Ian H Stevenson Hugo L Fernandes Iris Vilares Kunlin Wei Konrad P Körding |
| author_facet | Ian H Stevenson Hugo L Fernandes Iris Vilares Kunlin Wei Konrad P Körding |
| author_sort | Ian H Stevenson |
| collection | DOAJ |
| description | A large number of experiments have asked to what degree human reaching movements can be understood as being close to optimal in a statistical sense. However, little is known about whether these principles are relevant for other classes of movements. Here we analyzed movement in a task that is similar to surfing or snowboarding. Human subjects stand on a force plate that measures their center of pressure. This center of pressure affects the acceleration of a cursor that is displayed in a noisy fashion (as a cloud of dots) on a projection screen while the subject is incentivized to keep the cursor close to a fixed position. We find that salient aspects of observed behavior are well-described by optimal control models where a Bayesian estimation model (Kalman filter) is combined with an optimal controller (either a Linear-Quadratic-Regulator or Bang-bang controller). We find evidence that subjects integrate information over time taking into account uncertainty. However, behavior in this continuous steering task appears to be a highly non-linear function of the visual feedback. While the nervous system appears to implement Bayes-like mechanisms for a full-body, dynamic task, it may additionally take into account the specific costs and constraints of the task. |
| first_indexed | 2024-12-11T08:59:39Z |
| format | Article |
| id | doaj.art-513cf01893b141f79c6f37c819f1ca57 |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-12-11T08:59:39Z |
| publishDate | 2009-12-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-513cf01893b141f79c6f37c819f1ca572022-12-22T01:13:47ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582009-12-01512e100062910.1371/journal.pcbi.1000629Bayesian integration and non-linear feedback control in a full-body motor task.Ian H StevensonHugo L FernandesIris VilaresKunlin WeiKonrad P KördingA large number of experiments have asked to what degree human reaching movements can be understood as being close to optimal in a statistical sense. However, little is known about whether these principles are relevant for other classes of movements. Here we analyzed movement in a task that is similar to surfing or snowboarding. Human subjects stand on a force plate that measures their center of pressure. This center of pressure affects the acceleration of a cursor that is displayed in a noisy fashion (as a cloud of dots) on a projection screen while the subject is incentivized to keep the cursor close to a fixed position. We find that salient aspects of observed behavior are well-described by optimal control models where a Bayesian estimation model (Kalman filter) is combined with an optimal controller (either a Linear-Quadratic-Regulator or Bang-bang controller). We find evidence that subjects integrate information over time taking into account uncertainty. However, behavior in this continuous steering task appears to be a highly non-linear function of the visual feedback. While the nervous system appears to implement Bayes-like mechanisms for a full-body, dynamic task, it may additionally take into account the specific costs and constraints of the task.http://europepmc.org/articles/PMC2789327?pdf=render |
| spellingShingle | Ian H Stevenson Hugo L Fernandes Iris Vilares Kunlin Wei Konrad P Körding Bayesian integration and non-linear feedback control in a full-body motor task. PLoS Computational Biology |
| title | Bayesian integration and non-linear feedback control in a full-body motor task. |
| title_full | Bayesian integration and non-linear feedback control in a full-body motor task. |
| title_fullStr | Bayesian integration and non-linear feedback control in a full-body motor task. |
| title_full_unstemmed | Bayesian integration and non-linear feedback control in a full-body motor task. |
| title_short | Bayesian integration and non-linear feedback control in a full-body motor task. |
| title_sort | bayesian integration and non linear feedback control in a full body motor task |
| url | http://europepmc.org/articles/PMC2789327?pdf=render |
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