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From the Phenomenology to the Mechanisms of Consciousness: Integrated Information Theory 3.0

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  • Masafumi Oizumi
  • Larissa Albantakis
  • Giulio Tononi

Abstract

This paper presents Integrated Information Theory (IIT) of consciousness 3.0, which incorporates several advances over previous formulations. IIT starts from phenomenological axioms: information says that each experience is specific – it is what it is by how it differs from alternative experiences; integration says that it is unified – irreducible to non-interdependent components; exclusion says that it has unique borders and a particular spatio-temporal grain. These axioms are formalized into postulates that prescribe how physical mechanisms, such as neurons or logic gates, must be configured to generate experience (phenomenology). The postulates are used to define intrinsic information as “differences that make a difference” within a system, and integrated information as information specified by a whole that cannot be reduced to that specified by its parts. By applying the postulates both at the level of individual mechanisms and at the level of systems of mechanisms, IIT arrives at an identity: an experience is a maximally irreducible conceptual structure (MICS, a constellation of concepts in qualia space), and the set of elements that generates it constitutes a complex. According to IIT, a MICS specifies the quality of an experience and integrated information ΦMax its quantity. From the theory follow several results, including: a system of mechanisms may condense into a major complex and non-overlapping minor complexes; the concepts that specify the quality of an experience are always about the complex itself and relate only indirectly to the external environment; anatomical connectivity influences complexes and associated MICS; a complex can generate a MICS even if its elements are inactive; simple systems can be minimally conscious; complicated systems can be unconscious; there can be true “zombies” – unconscious feed-forward systems that are functionally equivalent to conscious complexes.Author Summary: Integrated information theory (IIT) approaches the relationship between consciousness and its physical substrate by first identifying the fundamental properties of experience itself: existence, composition, information, integration, and exclusion. IIT then postulates that the physical substrate of consciousness must satisfy these very properties. We develop a detailed mathematical framework in which composition, information, integration, and exclusion are defined precisely and made operational. This allows us to establish to what extent simple systems of mechanisms, such as logic gates or neuron-like elements, can form complexes that can account for the fundamental properties of consciousness. Based on this principled approach, we show that IIT can explain many known facts about consciousness and the brain, leads to specific predictions, and allows us to infer, at least in principle, both the quantity and quality of consciousness for systems whose causal structure is known. For example, we show that some simple systems can be minimally conscious, some complicated systems can be unconscious, and two different systems can be functionally equivalent, yet one is conscious and the other one is not.

Suggested Citation

  • Masafumi Oizumi & Larissa Albantakis & Giulio Tononi, 2014. "From the Phenomenology to the Mechanisms of Consciousness: Integrated Information Theory 3.0," PLOS Computational Biology, Public Library of Science, vol. 10(5), pages 1-25, May.
  • Handle: RePEc:plo:pcbi00:1003588
    DOI: 10.1371/journal.pcbi.1003588
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    References listed on IDEAS

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    1. Jeffrey A Edlund & Nicolas Chaumont & Arend Hintze & Christof Koch & Giulio Tononi & Christoph Adami, 2011. "Integrated Information Increases with Fitness in the Evolution of Animats," PLOS Computational Biology, Public Library of Science, vol. 7(10), pages 1-13, October.
    2. Adam B Barrett & Anil K Seth, 2011. "Practical Measures of Integrated Information for Time-Series Data," PLOS Computational Biology, Public Library of Science, vol. 7(1), pages 1-18, January.
    3. Christof Koch & Francis Crick, 2001. "The zombie within," Nature, Nature, vol. 411(6840), pages 893-893, June.
    4. David Balduzzi & Giulio Tononi, 2009. "Qualia: The Geometry of Integrated Information," PLOS Computational Biology, Public Library of Science, vol. 5(8), pages 1-24, August.
    5. Nihat Ay & Daniel Polani, 2008. "Information Flows In Causal Networks," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 11(01), pages 17-41.
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    Cited by:

    1. Francisco J Esteban & Javier A Galadí & José A Langa & José R Portillo & Fernando Soler-Toscano, 2018. "Informational structures: A dynamical system approach for integrated information," PLOS Computational Biology, Public Library of Science, vol. 14(9), pages 1-33, September.
    2. Takayuki Niizato & Kotaro Sakamoto & Yoh-ichi Mototake & Hisashi Murakami & Takenori Tomaru & Tomotaro Hoshika & Toshiki Fukushima, 2020. "Finding continuity and discontinuity in fish schools via integrated information theory," PLOS ONE, Public Library of Science, vol. 15(2), pages 1-29, February.
    3. repec:zna:indecs:v:19:y:2021:i:4:p:31-41 is not listed on IDEAS
    4. Soumya Banerjee, 2021. "Emergent rules of computation in the Universe lead to life and consciousness: a computational framework for consciousness," Interdisciplinary Description of Complex Systems - scientific journal, Croatian Interdisciplinary Society Provider Homepage: http://indecs.eu, vol. 19(1), pages 31-41.
    5. Max Tegmark, 2016. "Improved Measures of Integrated Information," PLOS Computational Biology, Public Library of Science, vol. 12(11), pages 1-34, November.
    6. Daniel Toker & Friedrich T Sommer, 2019. "Information integration in large brain networks," PLOS Computational Biology, Public Library of Science, vol. 15(2), pages 1-26, February.
    7. Peter Gordon Roetzel, 2019. "Information overload in the information age: a review of the literature from business administration, business psychology, and related disciplines with a bibliometric approach and framework developmen," Business Research, Springer;German Academic Association for Business Research, vol. 12(2), pages 479-522, December.
    8. Valmir C. Barbosa, 2017. "Information Integration from Distributed Threshold-Based Interactions," Complexity, Hindawi, vol. 2017, pages 1-14, January.
    9. Masafumi Oizumi & Shun-ichi Amari & Toru Yanagawa & Naotaka Fujii & Naotsugu Tsuchiya, 2016. "Measuring Integrated Information from the Decoding Perspective," PLOS Computational Biology, Public Library of Science, vol. 12(1), pages 1-18, January.
    10. David Engel & Thomas W Malone, 2018. "Integrated information as a metric for group interaction," PLOS ONE, Public Library of Science, vol. 13(10), pages 1-19, October.
    11. Antonio J. Ibáñez-Molina & Sergio Iglesias-Parro, 2018. "A Comparison between Theoretical and Experimental Measures of Consciousness as Integrated Information in an Anatomically Based Network of Coupled Oscillators," Complexity, Hindawi, vol. 2018, pages 1-8, April.

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