  We consider a living organism as an observer of the evolution of its
environment recording sensory information about the state space X of the
environment in real time. Sensory information is sampled and then processed on
two levels. On the biological level, the organism serves as an evaluation
mechanism of the subjective relevance of the incoming data to the observer: the
observer assigns excitation values to events in X it could recognize using its
sensory equipment. On the algorithmic level, sensory input is used for updating
a database, the memory of the observer whose purpose is to serve as a
geometric/combinatorial model of X, whose nodes are weighted by the excitation
values produced by the evaluation mechanism. These values serve as a guidance
system for deciding how the database should transform as observation data
mounts. We define a searching problem for the proposed model and discuss the
model's flexibility and its computational efficiency, as well as the
possibility of implementing it as a dynamic network of neuron-like units. We
show how various easily observable properties of the human memory and thought
process can be explained within the framework of this model. These include:
reasoning (with efficiency bounds), errors, temporary and permanent loss of
information. We are also able to define general learning problems in terms of
the new model, such as the language acquisition problem.
