Consciousness as a Functor

Fuente: arXiv
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Autore principale: Mahadevan, Sridhar
Natura: Preprint
Pubblicazione: 2025
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author Mahadevan, Sridhar
author_facet Mahadevan, Sridhar
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2508_17561
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Consciousness as a Functor
Mahadevan, Sridhar
Artificial Intelligence
Machine Learning
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.
title Consciousness as a Functor
topic Artificial Intelligence
Machine Learning
url https://arxiv.org/abs/2508.17561