Quasi-Conscious Multivariate Systems

TL;DR

This paper introduces a mathematical framework linking brain state biases and relationships through expected float entropy, suggesting that minimizing efe defines meaningful relationships that underpin conscious experience.

Contribution

It develops a novel theory connecting brain relationships, biases, and information measures using weighted relations and efe minimization, offering a new perspective on consciousness.

Findings

Monte-Carlo simulations show efe distributions with long left tails are significant.

The theory models relationships with weighted relations on nodes and states.

Minimizing efe leads to the emergence of meaningful relational structures.

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 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.