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10.1038/ncomms12176 http://scihub22266oqcxt.onion/10.1038/ncomms12176 C4942581!4942581!27397420     free     free     free Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Nat+Commun 2016 ; 7 (ä): ä Nephropedia Template TP {{tp|p=27397420|t=2016. Chunking as the result of an efficiency computation trade-off |pdf=|usr=}}{{27397420}} gab.com Text Chunking as the result of an efficiency computation trade-off JO: Nat Commun http://www.kidney.de/pget.php?&tw=1&pmid=27397420 #FOAvip 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. Twit Text FOAVip Chunking as the result of an efficiency computation trade-off ????Nat Commun http://www.kidney.de/pget.php?&tw=1&pmid=27397420 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=27397420 #FOAvip English Wikipedia <ref>{{Cite pmid|27397420}}</ref> Chunking as the result of an efficiency computation trade-off #MMPMID27397420 Ramkumar P; Acuna DE; Berniker M; Grafton ST; Turner RS; Kording KP Nat Commun 2016[]; 7 (ä): ä PMID27397420show ga 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. äChunking as the result of an efficiency computation trade-off (epmc).pdf DeepDyve Pubget Overpricing