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SUMMARY:Computational Neuroscience Journal Club - Hannah Sheahan (CBL)
DTSTART:20170404T150000Z
DTEND:20170404T160000Z
UID:TALK71930@talks.cam.ac.uk
CONTACT:Daniel McNamee
DESCRIPTION:Hannah Sheahan will cover:\n\n* Motor planning flexibly optimi
 zes performance under uncertainty about task goal\n* Aaron Wong\, Adrian H
 aith\n* Nature Communications (March 2017)\n* http://www.nature.com/articl
 es/ncomms14624\n\nAbstract:\nIn an environment full of potential goals\, h
 ow does the brain determine which movement to execute? Existing theories p
 osit that the motor system prepares for all potential goals by generating 
 several motor plans in parallel. One major line of evidence for such theor
 ies is that presenting two competing goals often results in a movement int
 ermediate between them. These intermediate movements are thought to reflec
 t an unintentional averaging of the competing plans. However\, normative t
 heories suggest instead that intermediate movements might actually be deli
 berate\, generated because they improve task performance over a random gue
 ssing strategy. To test this hypothesis\, we vary the benefit of making an
  intermediate movement by changing movement speed. We find that participan
 ts generate intermediate movements only at (slower) speeds where they meas
 urably improve performance. Our findings support the normative view that t
 he motor system selects only a single\, flexible motor plan\, optimized fo
 r uncertain goals.
LOCATION:Cambridge University Engineering Department\, CBL\, BE-438 (http:
 //learning.eng.cam.ac.uk/Public/Directions)
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