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Complexity as Causal Information Integration.

Carlotta Langer1, Nihat Ay1,2,3

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Summary
This summary is machine-generated.

This study introduces Causal Information Integration (ΦCII), a new measure for consciousness complexity in neural systems. ΦCII offers a calculable and intuitive approach to understanding causal connections, addressing limitations of prior methods.

Keywords:
causalitycomplexityconditional independenceem-algorithmintegrated information

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Integrated Information Theory

Background:

  • Integrated Information Theory (IIT) quantifies consciousness by measuring causal complexity in neural systems.
  • Existing measures often rely on minimizing KL-divergence, which can lack intuitive graphical representations.
  • The measure ΦCIS, based on conditional independence, is theoretically sound but difficult to analyze.

Purpose of the Study:

  • To develop a new measure of causal information integration that is both theoretically sound and practically analyzable.
  • To address the limitations of existing complexity measures within IIT, particularly the lack of graphical representation.
  • To propose a method that incorporates latent variables to model common external influences.

Main Methods:

  • Introduced a novel measure, Causal Information Integration (ΦCII), using a latent variable to represent common external influences.
  • Employed an iterative information geometric algorithm, specifically the Expectation-Maximization (EM) algorithm, for calculating ΦCII.
  • Compared the behavior and properties of ΦCII against existing integrated information measures.

Main Results:

  • The proposed ΦCII measure satisfies all desirable properties postulated for IIT complexity measures.
  • ΦCII provides a more intuitive and analyzable approach compared to measures like ΦCIS due to its graphical potential.
  • The EM-algorithm allows for practical computation and comparison of ΦCII with established measures.

Conclusions:

  • ΦCII represents a significant advancement in quantifying causal information integration within the framework of IIT.
  • This new measure offers a promising avenue for understanding the neural correlates of consciousness with improved analytical capabilities.
  • The latent variable approach and EM-algorithm facilitate a more comprehensive and accessible analysis of system complexity.