A refined computational interpretation of meta-representations in higher-order theories (HOT) of consciousness is proposed, focusing on process-level representations rather than mere transformations of first-order states. Meta-representations are argued to represent the computational processes that generate first-order representations, building on the Radical Plasticity Thesis. As a proof-of-concept, "meta-networks" were constructed using autoencoders of first-order neural networks within deep learning architectures, where latent spaces embedding first-order networks correspond to meta-representations. Applied to neural networks trained on visual and auditory datasets, these meta-representations successfully captured qualitative aspects by separating visual and auditory networks in the meta-representation space. This formulation offers an empirically testable hypothesis that brain regions may represent processes transforming one representation into another, potentially underlying the ability to describe qualia.
The paper introduces 'Universality' as a desirable property for theories of consciousness, borrowed from physics, where fundamental laws apply consistently everywhere. Universality requires that a theory can determine whether any fully described dynamical system is conscious or non-conscious, based on intrinsic properties rather than external interpretation. Most current theories lack this property, as they focus on neural correlates of consciousness in brain-centric systems. The authors argue that functionalist theories could become universal by specifying mathematical formulations of their concepts. While neurobiological and functionalist theories remain useful, a universal theory is needed to fully explain why certain systems possess consciousness.
Consciousness may have evolved as a platform for general intelligence—the ability to apply knowledge from past experiences to solve novel problems. The paper defines general intelligence and outlines three approaches to building AI systems that achieve it: simulation, combination, and generation. These correspond to proposed functions of consciousness from the information generation theory, global workspace theory, and a higher-order theory where qualia are meta-representations. The authors argue that consciousness integrates specialized generative models into a flexible complex, and that qualia allow an agent to choose which models to apply to new problems. These functions could be implemented as artificial consciousness, enabling systems to generate policies for novel problems with minimal trial and error.