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

Jonathan Mason

doi:10.3390/e21010060

Abstract

Over recent decades several mathematical theories of consciousness have been put forward including Karl Friston’s Free Energy Principle and Giulio Tononi’s Integrated Information Theory. In this article we further investigate theory based on Expected Float Entropy (EFE) minimisation which has been around since 2012. EFE involves a version of Shannon Entropy parameterised by relationships. It turns out that, for systems with bias due to learning, certain choices for the relationship parameters are isolated since giving much lower EFE values than others and, hence, the system defines relationships. It is proposed that, in the context of all these relationships, a brain state acquires meaning in the form of the relational content of the associated experience. EFE minimisation is itself an association learning process and its effectiveness as such is tested in this article. The theory and results are consistent with the proposition of there being a close connection between association learning processes and the emergence of consciousness. Such a theory may explain how the brain defines the content of consciousness up to relationship isomorphism.

Highlights

Consciousness is awash with relationships and associations which appear to be a fundamental aspect of conscious experience given that, for example, the colour red would lose much of its meaning if we couldn’t discern its relationship to the colour blue, a glass of water, the sound of a piano or anything else
This is the objective of the article Quasi-Conscious Multivariate Systems 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
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 emphasises the relevance of association learning processes to the emergence of consciousness; for example see [4]

Summary

Introduction

Consciousness is awash with relationships and associations which appear to be a fundamental aspect of conscious experience given that, for example, the colour red would lose much of its meaning if we couldn’t discern its relationship to the colour blue, a glass of water, the sound of a piano or anything else. Mathematics is 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. 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].

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Conclusion