FIAS Scientific Report 2011 - Frankfurt Institute for Advanced Studies ...

Learning mechanisms underlying visual orienting and other coordinated motor behaviors
Collaborators: S. Saeb 1 , C. Weber 2 , J. Triesch 1 , T. Weisswange 1,3 , C. Rothkopf 1 , T. Rodemann 3 , L. Lonini 1 ,
C. Dimitrakakis 1,4
1 Frankfurt Institute for Advanced Studies, 2 Dept. of Computer Science, University of Hamburg, 3 Honda Research
Institute Europe, Offenbach, 4 EPFL, Switzerland
Orienting movements are a fundamental class of motor behaviors that most animals must perform. In this
line of research we study the learning principles and mechanisms that may give rise to coordinated orienting
movements. On the one hand we are interested in how multiple sensory modalities such as vision and audition
are integrated for successful orienting movements. On the other hand we investigate how we learn to coordinate
multiple motor pathways such as eye and neck for successful orienting. More generally, we are asking how
motor control can be organized hierarchically and what this implies for learning complex behaviors, especially
when multiple behaviors have to be learned by the same system.
Of particular interest are saccades – rapid gaze shifts through which Human beings and many other species
redirect their gaze towards relevant targets. Saccades are made approximately three to four times every second,
and larger saccades result from fast and concurrent movement of the animal’s eyes and head. Experimental
studies have revealed that during saccades, the motor system follows certain principles such as respecting a
specific relationship between the relative contribution of eye and head motor systems to total gaze shift. Various
researchers have hypothesized that these principles are implications of some optimality criteria in the brain, but
it remains unclear how the brain can learn such an optimal behavior. We have proposed a new model that uses
a plausible learning mechanism to satisfy an optimality criterion. We show that after learning, the model is
able to reproduce motor behavior with biologically plausible properties. In addition, it predicts the nature of
the learning signals.
Figure: Architecture of our model learning coordinated
eye and head movements.
Related publications in 2011:
1) S. Saeb, C. Weber, J. Triesch, Learning the Optimal Control of Coordinated Eye and Head Movements, PLoS
Computational Biology, 7(11), doi:10.1371/ journal.pcbi.1002253, 2011.
2) H. Toutounji, C. A. Rothkopf, J. Triesch, Scalable reinforcement learning through hierarchical decompositions
for weakly-coupled problems, IEEE 10th International Conference on Development and Learning (ICDL),
August 24-27 (2011)
3) C. Karaoguz, T. H. Weisswange, T. Rodemann, B. Wrede, C. A. Rothkopf, Reward-based learning of optimal
cue integration in audio and visual depth estimation, 15th International Conference on Advanced Robotics
(ICAR), June 20-23 (2011)
4) T. H. Weisswange, C. A. Rothkopf, J. Triesch, Bayesian cue integration as a developmental outcome of
reward mediated learning, PLoS ONE, (2011), doi: 10.1371/journal.pone.0021575
5) C. A. Rothkopf, D. H. Ballard, Learning to coordinate repertoirs of behaviors: credit assignment and module
activation, submitted.
6) L. Lonini, C. Dimitrakakis, C. A. Rothkopf, J. Triesch, Generalization and interference in human motor
control, submitted.
83