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Advances in Integrated Information Theory

A topical collection in Entropy (ISSN 1099-4300). This collection belongs to the section "Information Theory, Probability and Statistics".

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Editors


E-Mail Website
Collection Editor
Department of Psychiatry, School of Medicine and Public Health, University of Wisconsin–Madison, 6001 Research Park Blvd, Madison, WI 53719, USA
Interests: causation and causal analysis; information; complex system science; (artificial) neural networks; machine learning; computational neuroscience; cognition; decision-making; artificial life/intelligence
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Collection Editor
Department of Psychiatry, School of Medicine and Public Health, University of Wisconsin–Madison, 6001 Research Park Blvd, Madison, WI 53719, USA
Interests: computational neuroscience; artificial neural networks; philosophy of mind; metaphysics; neuroethics; consciousness; causation; concepts; free will; artificial consciousness

E-Mail Website
Collection Editor
Department of Psychiatry, School of Medicine and Public Health, University of Wisconsin–Madison, 6001 Research Park Blvd, Madison, WI 53719, USA
Interests: perception; cognitive science; neuroimaging; phenomenology; conscious contents

Topical Collection Information

Dear Colleagues,

Integrated information theory (IIT) addresses the problem of consciousness and its physical substrate, providing a quantitative framework to analyze the compositional causal structure of (discrete) dynamical systems. In particular, IIT’s formalism is based on a notion of information that is physical and intrinsic (observer-independent) and a set of causal principles (“postulates”), including causal composition, specificity (“information”), irreducibility (“integration”), and definiteness (“exclusion”).

The IIT formalism offers a way to measure a system’s integrated information (Φ, “Phi”), which has been employed as a general measure of complexity that captures the extent to which a system is both differentiated and integrated. The IIT analysis allows one to identify a system’s causal borders and the organizational levels at which the system exhibits strong causal constraints. According to IIT, a physical substrate of consciousness must specify a maximum of Φ across elements, space, and time.

In addition to yielding a system’s Φ value, IIT’s causal analysis “unfolds” the compositional causal powers of a physical system in its current state. An important prediction of IIT is that the compositional causal structure of a system that specifies a maximum of Φ should account for the qualitative content of consciousness of that system.

For this Topical Collection, we invite contributions that apply, discuss, compare, or extend the theoretical framework of IIT. We also welcome submissions proposing approximations, practical measures, new applications, or alternative formulations of (parts of) the IIT formalism.

Following two Special Issues on IIT (“Integrated information theory” and “Integrated Information Theory and Consciousness”), this Topical Collection provides a repository for contributions that directly address IIT and its theoretical framework.

Dr. Larissa Albantakis
Dr. Matteo Grasso
Dr. Andrew Haun
Collection Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the collection website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • causal composition and higher order interactions
  • identifying causal/informational boundaries
  • causal exclusion and emergence
  • practical approximations of integrated information
  • mappings between phenomenology and compositional causal structures
  • applications

Published Papers (5 papers)

2023

Jump to: 2022

2 pages, 175 KiB  
Reply
Reply to Rourk, C. Comment on “Albantakis et al. Computing the Integrated Information of a Quantum Mechanism. Entropy 2023, 25, 449”
by Larissa Albantakis, Robert Prentner and Ian Durham
Entropy 2023, 25(10), 1442; https://doi.org/10.3390/e25101442 - 12 Oct 2023
Viewed by 534
Abstract
In response to a comment by Chris Rourk on our article Computing the Integrated Information of a Quantum Mechanism, we briefly (1) consider the role of potential hybrid/classical mechanisms from the perspective of integrated information theory (IIT), (2) discuss whether the (Q)IIT [...] Read more.
In response to a comment by Chris Rourk on our article Computing the Integrated Information of a Quantum Mechanism, we briefly (1) consider the role of potential hybrid/classical mechanisms from the perspective of integrated information theory (IIT), (2) discuss whether the (Q)IIT formalism needs to be extended to capture the hypothesized hybrid mechanism, and (3) clarify our motivation for developing a QIIT formalism and its scope of applicability. Full article
6 pages, 1249 KiB  
Comment
Comment on Albantakis et al. Computing the Integrated Information of a Quantum Mechanism. Entropy 2023, 25, 449
by Christopher Rourk
Entropy 2023, 25(10), 1436; https://doi.org/10.3390/e25101436 - 11 Oct 2023
Cited by 1 | Viewed by 809
Abstract
Integrated information theory (IIT) is a powerful tool that provides a framework for evaluating consciousness, whether in the human brain or in other systems. In Computing the Integrated Information of a Quantum Mechanism, the authors extend IIT from digital gates to a quantum [...] Read more.
Integrated information theory (IIT) is a powerful tool that provides a framework for evaluating consciousness, whether in the human brain or in other systems. In Computing the Integrated Information of a Quantum Mechanism, the authors extend IIT from digital gates to a quantum CNOT logic gate, and while they explicitly distinguish the analysis from quantum theories of consciousness, they nonetheless provide an analytical road map for extending IIT not only to other quantum mechanisms but also to hybrid computing structures like the brain. This comment provides additional information relating to an adiabatic quantum mechanical energy routing mechanism that is part of a hybrid biological computer that provides an action selection mechanism, which has been hypothesized to exist in the human brain and for which predicted evidence has been subsequently observed, and it hopes to motivate the further evaluation and extension of IIT not only to that hypothesized mechanism but also to other hybrid biological computers. Full article
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23 pages, 746 KiB  
Article
Computing the Integrated Information of a Quantum Mechanism
by Larissa Albantakis, Robert Prentner and Ian Durham
Entropy 2023, 25(3), 449; https://doi.org/10.3390/e25030449 - 3 Mar 2023
Cited by 5 | Viewed by 2933
Abstract
Originally conceived as a theory of consciousness, integrated information theory (IIT) provides a theoretical framework intended to characterize the compositional causal information that a system, in its current state, specifies about itself. However, it remains to be determined whether IIT as a theory [...] Read more.
Originally conceived as a theory of consciousness, integrated information theory (IIT) provides a theoretical framework intended to characterize the compositional causal information that a system, in its current state, specifies about itself. However, it remains to be determined whether IIT as a theory of consciousness is compatible with quantum mechanics as a theory of microphysics. Here, we present an extension of IIT’s latest formalism to evaluate the mechanism integrated information (φ) of a system subset to discrete, finite-dimensional quantum systems (e.g., quantum logic gates). To that end, we translate a recently developed, unique measure of intrinsic information into a density matrix formulation and extend the notion of conditional independence to accommodate quantum entanglement. The compositional nature of the IIT analysis might shed some light on the internal structure of composite quantum states and operators that cannot be obtained using standard information-theoretical analysis. Finally, our results should inform theoretical arguments about the link between consciousness, causation, and physics from the classical to the quantum. Full article
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15 pages, 2368 KiB  
Article
System Integrated Information
by William Marshall, Matteo Grasso, William G. P. Mayner, Alireza Zaeemzadeh, Leonardo S. Barbosa, Erick Chastain, Graham Findlay, Shuntaro Sasai, Larissa Albantakis and Giulio Tononi
Entropy 2023, 25(2), 334; https://doi.org/10.3390/e25020334 - 11 Feb 2023
Cited by 4 | Viewed by 2313
Abstract
Integrated information theory (IIT) starts from consciousness itself and identifies a set of properties (axioms) that are true of every conceivable experience. The axioms are translated into a set of postulates about the substrate of consciousness (called a complex), which are then used [...] Read more.
Integrated information theory (IIT) starts from consciousness itself and identifies a set of properties (axioms) that are true of every conceivable experience. The axioms are translated into a set of postulates about the substrate of consciousness (called a complex), which are then used to formulate a mathematical framework for assessing both the quality and quantity of experience. The explanatory identity proposed by IIT is that an experience is identical to the cause–effect structure unfolded from a maximally irreducible substrate (a Φ-structure). In this work we introduce a definition for the integrated information of a system (φs) that is based on the existence, intrinsicality, information, and integration postulates of IIT. We explore how notions of determinism, degeneracy, and fault lines in the connectivity impact system-integrated information. We then demonstrate how the proposed measure identifies complexes as systems, the φs of which is greater than the φs of any overlapping candidate systems. Full article
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2022

Jump to: 2023

25 pages, 2248 KiB  
Article
Non-Separability of Physical Systems as a Foundation of Consciousness
by Anton Arkhipov
Entropy 2022, 24(11), 1539; https://doi.org/10.3390/e24111539 - 26 Oct 2022
Cited by 3 | Viewed by 2484
Abstract
A hypothesis is presented that non-separability of degrees of freedom is the fundamental property underlying consciousness in physical systems. The amount of consciousness in a system is determined by the extent of non-separability and the number of degrees of freedom involved. Non-interacting and [...] Read more.
A hypothesis is presented that non-separability of degrees of freedom is the fundamental property underlying consciousness in physical systems. The amount of consciousness in a system is determined by the extent of non-separability and the number of degrees of freedom involved. Non-interacting and feedforward systems have zero consciousness, whereas most systems of interacting particles appear to have low non-separability and consciousness. By contrast, brain circuits exhibit high complexity and weak but tightly coordinated interactions, which appear to support high non-separability and therefore high amount of consciousness. The hypothesis applies to both classical and quantum cases, and we highlight the formalism employing the Wigner function (which in the classical limit becomes the Liouville density function) as a potentially fruitful framework for characterizing non-separability and, thus, the amount of consciousness in a system. The hypothesis appears to be consistent with both the Integrated Information Theory and the Orchestrated Objective Reduction Theory and may help reconcile the two. It offers a natural explanation for the physical properties underlying the amount of consciousness and points to methods of estimating the amount of non-separability as promising ways of characterizing the amount of consciousness. Full article
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