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Tytuł pozycji:

An error-tuned model for sensorimotor learning.

Tytuł :
An error-tuned model for sensorimotor learning.
Autorzy :
Ingram JN; Department of Engineering, University of Cambridge, Trumpington Street, Cambridge, United Kingdom.
Sadeghi M; Department of Engineering, University of Cambridge, Trumpington Street, Cambridge, United Kingdom.
Flanagan JR; Department of Psychology and Centre for Neuroscience Studies, Queen's University, Kingston, ON, Canada.
Wolpert DM; Department of Engineering, University of Cambridge, Trumpington Street, Cambridge, United Kingdom.
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Źródło :
PLoS computational biology [PLoS Comput Biol] 2017 Dec 18; Vol. 13 (12), pp. e1005883. Date of Electronic Publication: 2017 Dec 18 (Print Publication: 2017).
Typ publikacji :
Journal Article; Research Support, Non-U.S. Gov't
Język :
Imprint Name(s) :
Original Publication: San Francisco, CA : Public Library of Science, [2005]-
MeSH Terms :
Models, Psychological*
Motor Skills/*physiology
Adaptation, Physiological ; Biomechanical Phenomena ; Computational Biology ; Humans ; Memory/physiology ; Models, Neurological ; Psychomotor Performance/physiology ; Sensorimotor Cortex/physiology ; Task Performance and Analysis ; Visual Perception/physiology
References :
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Grant Information :
United Kingdom WT_ Wellcome Trust
Entry Date(s) :
Date Created: 20171219 Date Completed: 20180123 Latest Revision: 20210109
Update Code :
PubMed Central ID :
Czasopismo naukowe
Current models of sensorimotor control posit that motor commands are generated by combining multiple modules which may consist of internal models, motor primitives or motor synergies. The mechanisms which select modules based on task requirements and modify their output during learning are therefore critical to our understanding of sensorimotor control. Here we develop a novel modular architecture for multi-dimensional tasks in which a set of fixed primitives are each able to compensate for errors in a single direction in the task space. The contribution of the primitives to the motor output is determined by both top-down contextual information and bottom-up error information. We implement this model for a task in which subjects learn to manipulate a dynamic object whose orientation can vary. In the model, visual information regarding the context (the orientation of the object) allows the appropriate primitives to be engaged. This top-down module selection is implemented by a Gaussian function tuned for the visual orientation of the object. Second, each module's contribution adapts across trials in proportion to its ability to decrease the current kinematic error. Specifically, adaptation is implemented by cosine tuning of primitives to the current direction of the error, which we show to be theoretically optimal for reducing error. This error-tuned model makes two novel predictions. First, interference should occur between alternating dynamics only when the kinematic errors associated with each oppose one another. In contrast, dynamics which lead to orthogonal errors should not interfere. Second, kinematic errors alone should be sufficient to engage the appropriate modules, even in the absence of contextual information normally provided by vision. We confirm both these predictions experimentally and show that the model can also account for data from previous experiments. Our results suggest that two interacting processes account for module selection during sensorimotor control and learning.

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