Computational Neuroscience Journal Club

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• Hannah Sheahan (CBL)
• Tuesday 04 April 2017, 16:00-17:00
• Cambridge University Engineering Department, CBL, BE-438 (http://learning.eng.cam.ac.uk/Public/Directions).

If you have a question about this talk, please contact Daniel McNamee.

Hannah Sheahan will cover:

• Motor planning flexibly optimizes performance under uncertainty about task goal
• Aaron Wong, Adrian Haith
• Nature Communications (March 2017)
• http://www.nature.com/articles/ncomms14624

Abstract: In an environment full of potential goals, how does the brain determine which movement to execute? Existing theories posit that the motor system prepares for all potential goals by generating several motor plans in parallel. One major line of evidence for such theories is that presenting two competing goals often results in a movement intermediate between them. These intermediate movements are thought to reflect an unintentional averaging of the competing plans. However, normative theories suggest instead that intermediate movements might actually be deliberate, generated because they improve task performance over a random guessing strategy. To test this hypothesis, we vary the benefit of making an intermediate movement by changing movement speed. We find that participants generate intermediate movements only at (slower) speeds where they measurably improve performance. Our findings support the normative view that the motor system selects only a single, flexible motor plan, optimized for uncertain goals.

This talk is part of the Computational Neuroscience series.

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