  Learning and the ability to learn are important factors in development and
evolutionary processes [1]. Depending on the level, the complexity of learning
can strongly vary. While associative learning can explain simple learning
behaviour [1,2] much more sophisticated strategies seem to be involved in
complex learning tasks. This is particularly evident in machine learning theory
[3] (reinforcement learning [4], statistical learning [5]), but it equally
shows up in trying to model natural learning behaviour [2]. A general setting
for modelling learning processes in which statistical aspects are relevant is
provided by the neural network (NN) paradigm. This is in particular of interest
for natural, learning by experience situations. NN learning models can
incorporate elementary learning mechanisms based on neuro-physiological
analogies, such as the Hebb rule, and lead to quantitative results concerning
the dynamics of the learning process [6]. The Hebb rule, however, cannot be
directly applied in all cases, and in particular for realistic problems, such
as "delayed reinforcement" [4,6], the sophistication of the algorithms rapidly
increases. We want to present here a model which can cope with such non trivial
tasks, while still being elementary and based only on procedures which one may
think of as natural, without any appeal to higher strategies [7]. We can show
the capability of this model to provide good learning in many, very different
settings [7,8,9]. It may help therefore understanding some basic features of
learning.
