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SUMMARY:Understanding generative learning in the individual brain - Zoe Ko
 urtzi (University of Cambridge)
DTSTART:20160226T120000Z
DTEND:20160226T130000Z
UID:TALK62614@talks.cam.ac.uk
CONTACT:Kris Cao
DESCRIPTION:Learning from experience and adapting to new situations is fun
 damental to human development and wellbeing. Research on human learning ha
 s primarily focused on domain-specific skills (e.g. learning language\, sp
 orts). However\, a wide range of everyday behaviours—from detecting targ
 ets in clutter (e.g. finding a friend in the crowd)\, to navigating a new 
 city\, and co-ordinating our social interactions—critically depend on ex
 tracting meaningful structure from an initially incomprehensible stream of
  events. Extracting meaningful structures is critical for optimising not o
 nly our ability to interpret incoming information but also to predict upco
 ming events and react successfully to novel situations. We refer to this d
 omain-general skill of extracting the principles of organisation that dete
 rmine the structure of the environment\, as generative learning.\n\nTo und
 erstand individual ability for generative learning\, we combine behavioral
  and brain imaging measurements with computational modeling. Our work dete
 rmines prototypical strategies for generative learning and links individua
 l learning strategies to brain computations. Our behavioural findings sugg
 est that successful learning relies on extracting behaviorally relevant st
 ructures that are predictive of upcoming events rather than simply memoriz
 ing all possible statistical dependencies. Further\, we provide evidence t
 hat generative learning is implemented by a cortico-striatal circuit that 
 may select\, monitor and adjust individual learning strategies over time.\
 n\nWe propose that these brain computations may determine domain-general s
 trategies for generative learning that can become specialized to support h
 igher cognitive functions\, such as navigating a new city\, understanding 
 music and language.
LOCATION:FW26\, Computer Laboratory
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