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 inPloS one Vol. 12; no. 1; p. e0170466
Main Authors Acerbi, Luigi, Vijayakumar, Sethu, Wolpert, Daniel M.
Format Journal Article
LanguageEnglish
Published United States Public Library of Science 27.01.2017
Public Library of Science (PLoS)
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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.
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
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BackLink https://www.ncbi.nlm.nih.gov/pubmed/28129323$$D View this record in MEDLINE/PubMed
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2017 Acerbi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
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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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Snippet 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...
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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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https://pubmed.ncbi.nlm.nih.gov/PMC5271325
https://doaj.org/article/cac6ec025c2f4efd99b28982fa90b920
http://dx.doi.org/10.1371/journal.pone.0170466
Volume 12
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