Target Uncertainty Mediates Sensorimotor Error Correction
Human movements are prone to errors that arise from inaccuracies in both our perceptual processing and execution of motor commands. We can reduce such errors by both improving our estimates of the state of the world and through online error correction of the ongoing action. Two prominent frameworks...
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Published in | PloS one Vol. 12; no. 1; p. e0170466 |
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Language | English |
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Abstract | Human movements are prone to errors that arise from inaccuracies in both our perceptual processing and execution of motor commands. We can reduce such errors by both improving our estimates of the state of the world and through online error correction of the ongoing action. Two prominent frameworks that explain how humans solve these problems are Bayesian estimation and stochastic optimal feedback control. Here we examine the interaction between estimation and control by asking if uncertainty in estimates affects how subjects correct for errors that may arise during the movement. Unbeknownst to participants, we randomly shifted the visual feedback of their finger position as they reached to indicate the center of mass of an object. Even though participants were given ample time to compensate for this perturbation, they only fully corrected for the induced error on trials with low uncertainty about center of mass, with correction only partial in trials involving more uncertainty. The analysis of subjects' scores revealed that participants corrected for errors just enough to avoid significant decrease in their overall scores, in agreement with the minimal intervention principle of optimal feedback control. We explain this behavior with a term in the loss function that accounts for the additional effort of adjusting one's response. By suggesting that subjects' decision uncertainty, as reflected in their posterior distribution, is a major factor in determining how their sensorimotor system responds to error, our findings support theoretical models in which the decision making and control processes are fully integrated. |
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AbstractList | Human movements are prone to errors that arise from inaccuracies in both our perceptual processing and execution of motor commands. We can reduce such errors by both improving our estimates of the state of the world and through online error correction of the ongoing action. Two prominent frameworks that explain how humans solve these problems are Bayesian estimation and stochastic optimal feedback control. Here we examine the interaction between estimation and control by asking if uncertainty in estimates affects how subjects correct for errors that may arise during the movement. Unbeknownst to participants, we randomly shifted the visual feedback of their finger position as they reached to indicate the center of mass of an object. Even though participants were given ample time to compensate for this perturbation, they only fully corrected for the induced error on trials with low uncertainty about center of mass, with correction only partial in trials involving more uncertainty. The analysis of subjects’ scores revealed that participants corrected for errors just enough to avoid significant decrease in their overall scores, in agreement with the minimal intervention principle of optimal feedback control. We explain this behavior with a term in the loss function that accounts for the additional effort of adjusting one’s response. By suggesting that subjects’ decision uncertainty, as reflected in their posterior distribution, is a major factor in determining how their sensorimotor system responds to error, our findings support theoretical models in which the decision making and control processes are fully integrated. Human movements are prone to errors that arise from inaccuracies in both our perceptual processing and execution of motor commands. We can reduce such errors by both improving our estimates of the state of the world and through online error correction of the ongoing action. Two prominent frameworks that explain how humans solve these problems are Bayesian estimation and stochastic optimal feedback control. Here we examine the interaction between estimation and control by asking if uncertainty in estimates affects how subjects correct for errors that may arise during the movement. Unbeknownst to participants, we randomly shifted the visual feedback of their finger position as they reached to indicate the center of mass of an object. Even though participants were given ample time to compensate for this perturbation, they only fully corrected for the induced error on trials with low uncertainty about center of mass, with correction only partial in trials involving more uncertainty. The analysis of subjects' scores revealed that participants corrected for errors just enough to avoid significant decrease in their overall scores, in agreement with the minimal intervention principle of optimal feedback control. We explain this behavior with a term in the loss function that accounts for the additional effort of adjusting one's response. By suggesting that subjects' decision uncertainty, as reflected in their posterior distribution, is a major factor in determining how their sensorimotor system responds to error, our findings support theoretical models in which the decision making and control processes are fully integrated.Human movements are prone to errors that arise from inaccuracies in both our perceptual processing and execution of motor commands. We can reduce such errors by both improving our estimates of the state of the world and through online error correction of the ongoing action. Two prominent frameworks that explain how humans solve these problems are Bayesian estimation and stochastic optimal feedback control. Here we examine the interaction between estimation and control by asking if uncertainty in estimates affects how subjects correct for errors that may arise during the movement. Unbeknownst to participants, we randomly shifted the visual feedback of their finger position as they reached to indicate the center of mass of an object. Even though participants were given ample time to compensate for this perturbation, they only fully corrected for the induced error on trials with low uncertainty about center of mass, with correction only partial in trials involving more uncertainty. The analysis of subjects' scores revealed that participants corrected for errors just enough to avoid significant decrease in their overall scores, in agreement with the minimal intervention principle of optimal feedback control. We explain this behavior with a term in the loss function that accounts for the additional effort of adjusting one's response. By suggesting that subjects' decision uncertainty, as reflected in their posterior distribution, is a major factor in determining how their sensorimotor system responds to error, our findings support theoretical models in which the decision making and control processes are fully integrated. |
Audience | Academic |
Author | Wolpert, Daniel M. Vijayakumar, Sethu Acerbi, Luigi |
AuthorAffiliation | University of Exeter, UNITED KINGDOM 1 Institute of Perception, Action and Behaviour, School of Informatics, University of Edinburgh, Edinburgh, United Kingdom 2 Doctoral Training Centre in Neuroinformatics and Computational Neuroscience, School of Informatics, University of Edinburgh, Edinburgh, United Kingdom 3 Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, Cambridge, United Kingdom |
AuthorAffiliation_xml | – name: University of Exeter, UNITED KINGDOM – name: 3 Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, Cambridge, United Kingdom – name: 1 Institute of Perception, Action and Behaviour, School of Informatics, University of Edinburgh, Edinburgh, United Kingdom – name: 2 Doctoral Training Centre in Neuroinformatics and Computational Neuroscience, School of Informatics, University of Edinburgh, Edinburgh, United Kingdom |
Author_xml | – sequence: 1 givenname: Luigi orcidid: 0000-0001-7471-7336 surname: Acerbi fullname: Acerbi, Luigi – sequence: 2 givenname: Sethu surname: Vijayakumar fullname: Vijayakumar, Sethu – sequence: 3 givenname: Daniel M. surname: Wolpert fullname: Wolpert, Daniel M. |
BackLink | https://www.ncbi.nlm.nih.gov/pubmed/28129323$$D View this record in MEDLINE/PubMed |
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Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Conceptualization: LA SV DMW. Data curation: LA. Formal analysis: LA. Funding acquisition: LA SV DMW. Investigation: LA. Methodology: LA DMW. Project administration: LA. Resources: DMW. Software: LA. Supervision: SV DMW. Validation: LA. Visualization: LA. Writing – original draft: LA DMW. Writing – review & editing: LA SV DMW. Current address: Center for Neural Science, New York University, New York, NY, United States of America Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: SV received funding from Microsoft Research as Microsoft Senior Research Fellow in Learning Robotics. This does not alter our adherence to PLOS ONE policies on sharing data and materials. |
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SubjectTerms | Adaptation Adult Bayes Theorem Bayesian analysis Behavior Biology and Life Sciences Center of mass Commands Computer and Information Sciences Control systems Decision making Decision theory Earth Sciences Engineering and Technology Error correction Error correction & detection Error detection Estimates Feedback Feedback control Feedback, Sensory - physiology Female Fingers Humans Informatics Male Mathematical models Medicine and Health Sciences Movement - physiology Neurosciences Objectives Perceptual-motor processes Physical Sciences Physiological aspects Psychomotor Performance - physiology Research and Analysis Methods Sensorimotor system Social Sciences Stochasticity Studies Uncertainty analysis Visual perception |
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Title | Target Uncertainty Mediates Sensorimotor Error Correction |
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