  Conscious experience is awash with underlying relationships. Moreover, for
various brain regions such as the visual cortex, the system is biased toward
some states. Representing this bias using a probability distribution shows that
the system can define expected quantities. The mathematical theory in the
present paper links these facts by using expected float entropy (efe), which is
a measure of the expected amount of information needed, to specify the state of
the system, beyond what is already known about the system from relationships
that appear as parameters. Under the requirement that the relationship
parameters minimise efe, the brain defines relationships. It is proposed that
when a brain state is interpreted in the context of these relationships the
brain state acquires meaning in the form of the relational content of the
associated experience. For a given set, the theory represents relationships
using weighted relations which assign continuous weights, from 0 to 1, to the
elements of the Cartesian product of that set. The relationship parameters
include weighted relations on the nodes of the system and on their set of
states. Examples obtained using Monte-Carlo methods (where relationship
parameters are chosen uniformly at random) suggest that efe distributions with
long left tails are most important.
