  At the core of the Ouroboros Model lies a self-referential recursive process
with alternating phases of data acquisition and evaluation. Memory entries are
organized in schemata. Activation at a time of part of a schema biases the
whole structure and, in particular, missing features, thus triggering
expectations. An iterative recursive monitor process termed 'consumption
analysis' is then checking how well such expectations fit with successive
activations. A measure for the goodness of fit, "emotion", provides feedback as
(self-) monitoring signal. Contradictions between anticipations based on
previous experience and actual current data are highlighted as well as minor
gaps and deficits. The basic algorithm can be applied to goal directed
movements as well as to abstract rational reasoning when weighing evidence for
and against some remote theories. A sketch is provided how the Ouroboros Model
can shed light on rather different characteristics of human behavior including
learning and meta-learning. Partial implementations proved effective in
dedicated safety systems.
