  The Artificial Intelligence field is flooded with optimisation methods. In
this paper, we change the focus to developing modelling methods with the aim of
getting us closer to Artificial General Intelligence. To do so, we propose a
novel way to interpret reality as an information source, that is later
translated into a computational framework able to capture and represent such
information. This framework is able to build elements of classical cognitive
architectures, like Long Term Memory and Working Memory, starting from a simple
primitive that only processes Spatial Distributed Representations. Moreover, it
achieves such level of verticality in a seamless scalable hierarchical way.
