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SUMMARY:Hierarchical Neural Computations in Decision\, Action\, and Belief
  - Daniel McNamee\, University of Cambridge
DTSTART:20160418T131500Z
DTEND:20160418T141500Z
UID:TALK65610@talks.cam.ac.uk
CONTACT:Paula Kaanders
DESCRIPTION:At the algorithmic level\, neural computations can be describe
 d by the transmission\, manipulation\, and interaction of multiple distinc
 t representations within and across brain regions. In visual neuroscience\
 , decades of research has uncovered a hierarchy of successive and recurren
 t abstractions of incoming visual information from simple and complex codi
 ng in early visual cortex to sparse modality-independent "concept cells" i
 n the temporal lobe.  In this talk\, I will present work aimed at developi
 ng an analogous understanding in the domain of decision-making and control
  via the application of general linear modeling (based on behavioral model
 s)\, multivoxel pattern decoding\, and connectivity analysis to functional
  magnetic resonance imaging data. Through combinations of these techniques
 \, nontrivial intermediate representations and hierarchical modes of neura
 l processing will be shown to be utilized by the brain in the production o
 f rational behavior.  Together\, the results of these studies constrain th
 e set of possible algorithms being implemented by the brain in decision-ma
 king\, action selection\, and abstract inference.
LOCATION:Kenneth Craik Room\, Craik Marshall Building\, Downing Site\, Cam
 bridge
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