  The aim of this paper is to study the relevance of simplicity and its formal
representation as Kolmogorov or algorithmic complexity in the cognitive
sciences. The discussion is based on two premises: 1) all human experience is
generated in the brain, 2) the brain has only access to information. Taken
together, these two premises lead us to conclude that all the elements of what
we call `reality' are derived mental constructs based on information and
compression, i.e., algorithmic models derived from the search for simplicity in
data. Naturally, these premises apply to humans in real or virtual environments
as well as robots or other cognitive systems. Based on this, it is further
hypothesized that there is a hierarchy of processing levels where simplicity
and compression play a major role. As applications, I illustrate first the
relevance of compression and simplicity in fundamental neuroscience with an
analysis of the Mismatch Negativity paradigm. Then I discuss the applicability
to Presence research, which studies how to produce real-feeling experiences in
mediated interaction, and use Bayesian modeling to define in a formal way
different aspects of the illusion of Presence. The idea is put forth that given
alternative models (interpretations) for a given mediated interaction, a brain
will select the simplest one it can construct weighted by prior models. In the
final section the universality of these ideas and applications in robotics,
machine learning, biology and education is discussed. I emphasize that there is
a common conceptual thread based on the idea of simplicity, which suggests a
common study approach.
