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SUMMARY:An engineering approach to aversive learning. - Dr Ben Seymour\, W
 ellcome Trust Intermediate Clinical Fellow\, Department of Engineering\, U
 niversity of Cambridge
DTSTART:20160506T153000Z
DTEND:20160506T170000Z
UID:TALK65571@talks.cam.ac.uk
CONTACT:Louise White
DESCRIPTION:Humans and animals learn to predict and escape from or avoid p
 otential threats by a combination of Pavlovian and instrumental learning. 
 In recent years\, reinforcement learning theory has provided a computation
 al framework that has allowed mechanistic\, quantitative models of the und
 erlying learning processes\, and this can be used to understand the underl
 ying brain systems that implement them (for example\, using model-based fM
 RI). This has been used to identify\, amongst other things\, a key roe for
  the striatum in the representation of Pavlovian and instrumental predicti
 on errors for punishments. More recently\, we have been trying to understa
 nd how such accounts of motivational learning control not only responses a
 nd actions towards punishments\, but also the subjective experience of the
 m: for instance in the case of pain\, asking whether endogenous analgesia 
 comes under the control aversive motivational systems. This sort of accoun
 t should yield innovation of novel interventional strategies to treat diso
 rders of pain and fear.\n\n
LOCATION:Ground Floor Lecture Theatre\, Department of Psychology
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