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Ryota Kanai1, Ryota Takatsuki2,3,4, Ippei Fujisawa5

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This study introduces a computational model for meta-representations, crucial for consciousness theories. The novel "meta-networks" represent computational processes, offering a new way to understand sensory information transformation and qualia.

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Area of Science:

  • Cognitive Science
  • Computational Neuroscience
  • Artificial Intelligence
  • Philosophy of Mind

Background:

  • Higher-order theories (HOT) of consciousness propose that consciousness arises from meta-representations, which are representations of first-order sensory data.
  • Implementing abstract meta-representational concepts into concrete computational models, particularly in artificial intelligence, presents significant theoretical and practical challenges.
  • Previous interpretations of meta-representation as simple representational transformations or confidence estimates are considered insufficient and potentially trivial.

Purpose of the Study:

  • To propose a refined computational interpretation of meta-representations focusing on process-level representations, moving beyond simplistic views.
  • To develop a proof-of-concept computational model, termed 'meta-networks,' to explicitly implement and test this refined notion of meta-representation.
  • To explore the potential of meta-representations in understanding the qualitative aspects of sensory experience (qualia) and their neural basis.

Main Methods:

  • Developed 'meta-networks' by implementing autoencoders of first-order neural networks within deep learning architectures.
  • Methodologically isolated process representations from specific sensory activations to prevent confounding effects in the model.
  • Trained meta-networks on visual and auditory datasets to embed first-order neural networks, with latent spaces representing meta-representations.

Main Results:

  • Meta-representations successfully captured qualitative aspects of the first-order networks, demonstrated by the separation of visual and auditory networks in the meta-representation space.
  • The proposed meta-networks provide a method for quantitatively comparing and contrasting the qualitative differences between computational processes.
  • The study validates the refined, process-oriented view of meta-representation as representing the computational processes underlying first-order representations.

Conclusions:

  • The developed meta-networks offer an empirically testable hypothesis for neural mechanisms representing representational transformation processes.
  • This computational formulation of meta-representation may underlie the human ability to describe the qualitative aspects of sensory experience (qualia).
  • The study provides a foundational step towards computationally modeling higher-order theories of consciousness and their link to subjective experience.