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

A mathematical theory of consciousness based on Expected Float Entropy (EFE) minimisation is further investigated. EFE is a version of Shannon Entropy parameterised by relationships. For systems with learning-induced bias, certain relationship parameter choices yield much lower EFE values, thereby defining relationships. In this context, a brain state acquires meaning through the relational content of associated experience. EFE minimisation is tested as an association learning process, and results support a close connection between such learning and the emergence of consciousness. The theory may explain how the brain defines conscious content up to relationship isomorphism.

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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.