Abstract:
: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.
journal_name
Nat Communjournal_title
Nature communicationsauthors
Ramkumar P,Acuna DE,Berniker M,Grafton ST,Turner RS,Kording KPdoi
10.1038/ncomms12176subject
Has Abstractpub_date
2016-07-11 00:00:00pages
12176issn
2041-1723pii
ncomms12176journal_volume
7pub_type
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