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SUMMARY:Computational Neuroscience Journal Club - Mohsen Sadeghi (CBL)
DTSTART:20170124T160000Z
DTEND:20170124T170000Z
UID:TALK70519@talks.cam.ac.uk
CONTACT:Daniel McNamee
DESCRIPTION:Mohsen Sadeghi will cover:\n\n* Generalization of prior inform
 ation for rapid Bayesian time estimation\n* Neil W. Roach\, Paul V. McGraw
 a\, David J. Whitaker\, James Heron\n* PNAS (January 2017)\n* http://www.p
 nas.org/content/114/2/412.full\n(abstract included below)\n\nAbstract:\nTo
  enable effective interaction with the environment\, the brain combines no
 isy sensory information with expectations based on prior experience. There
  is ample evidence showing that humans can learn statistical regularities 
 in sensory input and exploit this knowledge to improve perceptual decision
 s and actions. However\, fundamental questions remain regarding how priors
  are learned and how they generalize to different sensory and behavioral c
 ontexts. In principle\, maintaining a large set of highly specific priors 
 may be inefficient and restrict the speed at which expectations can be for
 med and updated in response to changes in the environment. However\, prior
 s formed by generalizing across varying contexts may not be accurate. Here
 \, we exploit rapidly induced contextual biases in duration reproduction t
 o reveal how these competing demands are resolved during the early stages 
 of prior acquisition. We show that observers initially form a single prior
  by generalizing across duration distributions coupled with distinct senso
 ry signals. In contrast\, they form multiple priors if distributions are c
 oupled with distinct motor outputs. Together\, our findings suggest that r
 apid prior acquisition is facilitated by generalization across experiences
  of different sensory inputs but organized according to how that sensory i
 nformation is acted on.
LOCATION:Cambridge University Engineering Department\, CBL\, BE-438 (http:
 //learning.eng.cam.ac.uk/Public/Directions)
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