Literature DB >> 28202784

Learning to Predict and Control the Physics of Our Movements.

Reza Shadmehr1.   

Abstract

When we hold an object in our hand, the mass of the object alters the physics of our arm, changing the relationship between motor commands that our brain sends to our arm muscles and the resulting motion of our hand. If the object is unfamiliar to us, our first movement will exhibit an error, producing a trajectory that is different from the one we had intended. This experience of error initiates learning in our brain, making it so that on the very next attempt our motor commands partially compensate for the unfamiliar physics, resulting in smaller errors. With further practice, the compensation becomes more complete, and our brain forms a model that predicts the physics of the object. This model is a motor memory that frees us from having to relearn the physics the next time that we encounter the object. The mechanism by which the brain transforms sensory prediction errors into corrective motor commands is the basis for how we learn the physics of objects with which we interact. The cerebellum and the motor cortex appear to be critical for our ability to learn physics, allowing us to use tools that extend our capabilities, making us masters of our environment.
Copyright © 2017 the authors 0270-6474/17/371663-09$15.00/0.

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Year:  2017        PMID: 28202784      PMCID: PMC5320601          DOI: 10.1523/JNEUROSCI.1675-16.2016

Source DB:  PubMed          Journal:  J Neurosci        ISSN: 0270-6474            Impact factor:   6.167


  72 in total

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  16 in total

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5.  Random Practice Enhances Retention and Spatial Transfer in Force Field Adaptation.

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6.  Tract profiles of the cerebellar peduncles in children who stutter.

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8.  Adaptive changes in automatic motor responses based on acquired visuomotor correspondence.

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9.  Neural Substrates of Muscle Co-contraction during Dynamic Motor Adaptation.

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Journal:  J Neurosci       Date:  2021-06-04       Impact factor: 6.167

10.  Identifying the neural representation of fast and slow states in force field adaptation via fMRI.

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