BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Talks.cam//talks.cam.ac.uk//
X-WR-CALNAME:Talks.cam
BEGIN:VEVENT
SUMMARY:Mapping affective decisions in depression using reinforcement lear
 ning tools - Quentin Huys (Gatsby Computational Neuroscience Unit\, UCL)
DTSTART:20100505T153000Z
DTEND:20100505T163000Z
UID:TALK24760@talks.cam.ac.uk
CONTACT:Prof Máté Lengyel
DESCRIPTION:Most decision problems the brain faces are fantastically hard.
  I will describe three different general solution strategies that are well
  described at neural\, behavioural and computational level\, and show how 
 each of these can elucidate important aspects of decision making in depres
 sion. First\, concentrating on habitual learning\, we will apply a simple 
 TD-like learning model to a large dataset of 392 subjects in a simple asym
 metrically rewarded decision making task (due to Diego Pizzagalli). This a
 llows for a detailed analysis relating the effect of dopamine\, stress and
  depression to the processing of rewards. Second\, we will present a Bayes
 ian model of learned helplessness. We show that the prior belief about the
  extent to which the environment is controllable has profound impacts on g
 oal-directed choice behaviour\, and that this is related specifically to p
 sychometric measures of hopelessness in subjects suffering from recurrent 
 depression. Finally\, we will discuss the role of serotonin in behavioural
  inhibition. Computationally	separating the effects of behavioural inhibit
 ion during learning and later during behaviour allows for insights into th
 e prozac paradox: the fact that	5HTTLPR polymorphisms and SSRIs seem to do
  the same yet have opposite effects on depression. Furthermore\, consideri
 ng the relationship between serotonin and innate\, evolutionarily acquired
  Pavlovian responses hints at a possible reason for the co-morbidities bet
 ween mood and anxiety disorders.
LOCATION:Cambridge University Engineering Department\, CBL Rm #438 (http:/
 /learning.eng.cam.ac.uk/Public/Directions)
END:VEVENT
END:VCALENDAR