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SUMMARY:Visuomotor behavior in naturalistic tasks: from receptive fields t
 o value functions - Constantin Rothkopf\, Frankfurt Institute for Advanced
  Studies
DTSTART:20120503T103000Z
DTEND:20120503T113000Z
UID:TALK37983@talks.cam.ac.uk
CONTACT:Dr. Cristina Savin
DESCRIPTION:Although there is a long tradition of separating perception\, 
 action\, and learning\, these can be treated separately only under very sp
 ecial circumstances. There are both theoretical reasons evident from a tre
 atment of such control tasks in the framework of Markov decision processes
 \, as well as empirical studies having revealed this fact\, especially whe
 n considering naturalistic sequential visuomotor tasks. We will present re
 sults from several studies investigating these\ndependencies.\n\nFirst\, w
 e show that learning of representations of natural visual stimuli through 
 generative models can explain a variety of psychophysical biases only when
  in addition to the statistics of the natural environment one also takes t
 he influence of the specific imaging system such as the eye as well as the
  active usage of the visual system into account.\n\nSecond\, we will show 
 how human visuomotor behavior can be quantified using Bayesian inverse rei
 nforcement learning algorithms to extract the reward functions underlying 
 human actions. This analysis demonstrates that the guidance behavior in a 
 navigation task does not necessarily follow the given task instructions an
 d reveals systematic individual differences within subject’s task priori
 ties.\n\nFinally\, we will present results from a study in which human sub
 jects\nintercepted moving objects in a virtual environment. Many animals u
 se the well studied constant bearing angle strategy\, which has been chara
 cterized as a fast and frugal heuristic. The relationships governing the b
 ehavior of the environment were manipulated systematically so as to reveal
  that subjects can indeed quickly learn new sequential control policies. A
  theoretical analysis shows\, that the learned behavior can only be unders
 tood by considering the observation and control uncertainties in order to 
 successfully carry out the interception task.
LOCATION:Cambridge University Engineering Department\, CBL Rm #438 (http:/
 /learning.eng.cam.ac.uk/Public/Directions)
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