Consciousness as a Functor
This work addresses the fundamental challenge of understanding consciousness in cognitive science, but it appears incremental as it builds on existing theories like Global Workspace Theory.
The authors tackled the problem of modeling consciousness by proposing a functor-based theory that integrates unconscious and conscious memory processes, resulting in a categorial formulation of Global Workspace Theory with components like MUMBLE and URL.
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.