Integrated world modeling theory expanded: Implications for the future of consciousness

• Published in: Frontiers in computational neuroscience Vol. 16; p. 642397
• Main Author: Safron, Adam
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 24.11.2022; Frontiers Media S.A
• Subjects: Associative learning; Bayesian analysis; Consciousness; expander graphs; Free energy; Free Energy Principle and Active Inference (FEP-AI) Framework; Game theory; Global Neuronal Workspace Theory (GNWT); Information theory; Integrated Information Theory (IIT); Mathematical models; Neural networks; Neuroscience; predictive turbo autoencoding; Sensory integration; Integrated Information Theory (IIT); consciousness; predictive turbo autoencoding; Free Energy Principle and Active Inference (FEP-AI) Framework; Global Neuronal Workspace Theory (GNWT); shared latent spaces; expander graphs; Graph Neural Networks (GNNs)
• ISSN: 1662-5188; 1662-5188
• DOI: 10.3389/fncom.2022.642397

Integrated world modeling theory (IWMT) is a synthetic theory of consciousness that uses the free energy principle and active inference (FEP-AI) framework to combine insights from integrated information theory (IIT) and global neuronal workspace theory (GNWT). Here, I first review philosophical principles and neural systems contributing to IWMT’s integrative perspective. I then go on to describe predictive processing models of brains and their connections to machine learning architectures, with particular emphasis on autoencoders (perceptual and active inference), turbo-codes (establishment of shared latent spaces for multi-modal integration and inferential synergy), and graph neural networks (spatial and somatic modeling and control). Future directions for IIT and GNWT are considered by exploring ways in which modules and workspaces may be evaluated as both complexes of integrated information and arenas for iterated Bayesian model selection. Based on these considerations, I suggest novel ways in which integrated information might be estimated using concepts from probabilistic graphical models, flow networks, and game theory. Mechanistic and computational principles are also considered with respect to the ongoing debate between IIT and GNWT regarding the physical substrates of different kinds of conscious and unconscious phenomena. I further explore how these ideas might relate to the “Bayesian blur problem,” or how it is that a seemingly discrete experience can be generated from probabilistic modeling, with some consideration of analogies from quantum mechanics as potentially revealing different varieties of inferential dynamics. I go on to describe potential means of addressing critiques of causal structure theories based on network unfolding, and the seeming absurdity of conscious expander graphs (without cybernetic symbol grounding). Finally, I discuss future directions for work centered on attentional selection and the evolutionary origins of consciousness as facilitated “unlimited associative learning.” While not quite solving the Hard problem, this article expands on IWMT as a unifying model of consciousness and the potential future evolution of minds.