Quasi‐conscious multivariate systems

Abstract

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 this article links these facts 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 minimize 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.

From Learning to Consciousness: An Example Using Expected Float Entropy Minimisation

“…Moreover, mathematics is also awash with relationships and this suggests a mathematical theory for how the brain defines the relational content of consciousness could well be possible. This is the objective of the article Quasi-Conscious Multivariate Systems (see reference [ 1 ]) published in 2016 which greatly developed the theory first mentioned in reference [ 2 ] and is based on expected float entropy minimisation, the definition of which is given below. The theory also has potential applications in areas such as artificial intelligence and mutual or effective interaction analysis in nerve fibres; see reference [ 3 ].…”

“…In the present article we investigate the coincidence that whilst expected float entropy minimisation was developed as a way to uncover the relationships systems define, it is itself a learning process and this fact (at least in the context of the theory presented in [ 1 ]) emphasises the relevance of association learning processes to the emergence of consciousness; for example see [ 4 ]. Furthermore, for brain structures involved in generating consciousness that are configured by a biologically implemented association learning process, it is interesting to consider the extent to which the learning process and expected float entropy minimisation are analogous; [ 5 ].…”

“…In the present article we focus on testing expected float entropy minimisation to see how effective an association learning process it actually is. We already know from [ 1 ] that expected float entropy minimisation uncovers relationships that systems define but we wish to test the extent and significance of the relationships uncovered. To do this we apply expected float entropy minimisation to a set of data to obtain relationships and then apply the relationships learned to a data completion task that is analogous to the brain’s ability for filling in missing information.…”

“…The performance is then compared with a state of the art black box using the neural fitting tool in MATLAB which is applied to the same data completion task. We will now recall the basics of the theory presented in [ 1 ].…”

“…The theory in [ 1 ] provides a starting point for a mathematical theory of how the brain defines all of the relationships underlying consciousness. It proposes that a brain state in the context of all these relationships, defined by the brain, has meaning in the form of the relational content of the associated experience.…”