Consciousness as a Functor

Consciousness can be modeled as a mathematical functor that transfers contents between unconscious and conscious memory, offering a formal version of the Global Workspace Theory. The framework represents unconscious processes as a topos category of coalgebras and defines an internal language of thought called MUMBLE. Information flow from conscious short-term working memory to long-term unconscious memory is described using Universal Reinforcement Learning, while the reverse transmission from unconscious long-term memory into resource-constrained short-term memory is modeled with a network economic approach.

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Abstract

We propose a novel theory of consciousness as a functor (CF) that receives and transmits contents from unconscious memory into conscious memory. Our CF framework can be seen as a categorial formulation of the Global Workspace Theory proposed by Baars. CF models the ensemble of unconscious processes as a topos category of coalgebras. The internal language of thought in CF is defined as a Multi-modal Universal Mitchell-Benabou Language Embedding (MUMBLE). We model the transmission of information from conscious short-term working memory to long-term unconscious memory using our recently proposed Universal Reinforcement Learning (URL) framework. To model the transmission of information from unconscious long-term memory into resource-constrained short-term memory, we propose a network economic model.