Visuomotor behavior in naturalistic task: from receptive fields to value functions

13 November 2012
Begin time: 
End time: 
ZiF (Long Table Room)

Although there is a long tradition of separating perception, action, and learning,
these can be treated separately only under very special circumstances. There are
both theoretical reasons evident from a treatment of such control tasks in the
framework of Markov decision processes, as well as empirical studies having
revealed this fact, especially when considering naturalistic sequential visuomotor
tasks. We will present results from several studies investigating these
First, we show that learning of representations of natural visual stimuli through
generative models can explain a variety of psychophysical biases only when
the statistics of the natural environment and the active usage of the visual
system are taken into account.
Secondly, we will show how human visuomotor behavior can be quantified
using Bayesian inverse reinforcement 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 and reveals systematic individual differences
within subject’s task priorities. We will also show, how eye-movements
can be related to the estimated task priorities.
Finally, we will present results from a study in which human subjects
intercepted moving objects in a virtual environment. The probabilistic
relationships governing the behavior 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 understood by considering the observation and control
uncertainties in order to successfully carry out the interception task.