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Chunking as the result of an efficiency computation trade-off

How to move efficiently is an optimal control problem, whose computational complexity grows exponentially with the horizon of the planned trajectory. Breaking a compound movement into a series of chunks, each planned over a shorter horizon can thus reduce the overall computational complexity and ass...

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Autores principales: Ramkumar, Pavan, Acuna, Daniel E., Berniker, Max, Grafton, Scott T., Turner, Robert S., Kording, Konrad P.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4942581/
https://www.ncbi.nlm.nih.gov/pubmed/27397420
http://dx.doi.org/10.1038/ncomms12176
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author Ramkumar, Pavan
Acuna, Daniel E.
Berniker, Max
Grafton, Scott T.
Turner, Robert S.
Kording, Konrad P.
author_facet Ramkumar, Pavan
Acuna, Daniel E.
Berniker, Max
Grafton, Scott T.
Turner, Robert S.
Kording, Konrad P.
author_sort Ramkumar, Pavan
collection PubMed
description How to move efficiently is an optimal control problem, whose computational complexity grows exponentially with the horizon of the planned trajectory. Breaking a compound movement into a series of chunks, each planned over a shorter horizon can thus reduce the overall computational complexity and associated costs while limiting the achievable efficiency. This trade-off suggests a cost-effective learning strategy: to learn new movements we should start with many short chunks (to limit the cost of computation). As practice reduces the impediments to more complex computation, the chunking structure should evolve to allow progressively more efficient movements (to maximize efficiency). Here we show that monkeys learning a reaching sequence over an extended period of time adopt this strategy by performing movements that can be described as locally optimal trajectories. Chunking can thus be understood as a cost-effective strategy for producing and learning efficient movements.
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spelling pubmed-49425812016-09-20 Chunking as the result of an efficiency computation trade-off Ramkumar, Pavan Acuna, Daniel E. Berniker, Max Grafton, Scott T. Turner, Robert S. Kording, Konrad P. Nat Commun Article How to move efficiently is an optimal control problem, whose computational complexity grows exponentially with the horizon of the planned trajectory. Breaking a compound movement into a series of chunks, each planned over a shorter horizon can thus reduce the overall computational complexity and associated costs while limiting the achievable efficiency. This trade-off suggests a cost-effective learning strategy: to learn new movements we should start with many short chunks (to limit the cost of computation). As practice reduces the impediments to more complex computation, the chunking structure should evolve to allow progressively more efficient movements (to maximize efficiency). Here we show that monkeys learning a reaching sequence over an extended period of time adopt this strategy by performing movements that can be described as locally optimal trajectories. Chunking can thus be understood as a cost-effective strategy for producing and learning efficient movements. Nature Publishing Group 2016-07-11 /pmc/articles/PMC4942581/ /pubmed/27397420 http://dx.doi.org/10.1038/ncomms12176 Text en Copyright © 2016, The Author(s) http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
spellingShingle Article
Ramkumar, Pavan
Acuna, Daniel E.
Berniker, Max
Grafton, Scott T.
Turner, Robert S.
Kording, Konrad P.
Chunking as the result of an efficiency computation trade-off
title Chunking as the result of an efficiency computation trade-off
title_full Chunking as the result of an efficiency computation trade-off
title_fullStr Chunking as the result of an efficiency computation trade-off
title_full_unstemmed Chunking as the result of an efficiency computation trade-off
title_short Chunking as the result of an efficiency computation trade-off
title_sort chunking as the result of an efficiency computation trade-off
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4942581/
https://www.ncbi.nlm.nih.gov/pubmed/27397420
http://dx.doi.org/10.1038/ncomms12176
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