IDEAS home Printed from https://ideas.repec.org/a/plo/pcbi00/1003588.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003588
    Download Restriction: no

    File URL: https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1003588&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pcbi.1003588?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    3. Valmir C. Barbosa, 2017. "Information Integration from Distributed Threshold-Based Interactions," Complexity, Hindawi, vol. 2017, pages 1-14, January.
    4. 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.
    5. 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.
    6. repec:zna:indecs:v:19:y:2021:i:4:p:31-41 is not listed on IDEAS
    7. 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.
    8. Max Tegmark, 2016. "Improved Measures of Integrated Information," PLOS Computational Biology, Public Library of Science, vol. 12(11), pages 1-34, November.
    9. 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.
    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. 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.
    3. 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.
    4. Max Tegmark, 2016. "Improved Measures of Integrated Information," PLOS Computational Biology, Public Library of Science, vol. 12(11), pages 1-34, November.
    5. Werner, Gerhard, 2013. "Consciousness viewed in the framework of brain phase space dynamics, criticality, and the Renormalization Group," Chaos, Solitons & Fractals, Elsevier, vol. 55(C), pages 3-12.
    6. Gifford Jr., Adam, 2009. "Cultural, cognition and human action," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 38(1), pages 13-24, January.
    7. 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.
    8. Rajan, Rishabh & Rana, Nripendra P. & Parameswar, Nakul & Dhir, Sanjay & Sushil, & Dwivedi, Yogesh K., 2021. "Developing a modified total interpretive structural model (M-TISM) for organizational strategic cybersecurity management," Technological Forecasting and Social Change, Elsevier, vol. 170(C).
    9. Adam B Barrett & Michael Murphy & Marie-Aurélie Bruno & Quentin Noirhomme & Mélanie Boly & Steven Laureys & Anil K Seth, 2012. "Granger Causality Analysis of Steady-State Electroencephalographic Signals during Propofol-Induced Anaesthesia," PLOS ONE, Public Library of Science, vol. 7(1), pages 1-12, January.
    10. 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.
    11. Shanmugavel, Nagarajan & Balakrishnan, Janarthanan, 2023. "Influence of pro-environmental behaviour towards behavioural intention of electric vehicles," Technological Forecasting and Social Change, Elsevier, vol. 187(C).
    12. Temel, Tugrul & Phumpiu, Paul, 2023. "Policy Design from a Network Perspective: Targeting a Sector, Cascade of Links, Network Resilience," MPRA Paper 118466, University Library of Munich, Germany.
    13. Adam Gifford, 2007. "The knowledge problem, determinism, and The Sensory Order," The Review of Austrian Economics, Springer;Society for the Development of Austrian Economics, vol. 20(4), pages 269-291, December.
    14. William G P Mayner & William Marshall & Larissa Albantakis & Graham Findlay & Robert Marchman & Giulio Tononi, 2018. "PyPhi: A toolbox for integrated information theory," PLOS Computational Biology, Public Library of Science, vol. 14(7), pages 1-21, July.
    15. 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.
    16. van Elteren, Casper & Quax, Rick & Sloot, Peter, 2022. "Dynamic importance of network nodes is poorly predicted by static structural features," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 593(C).
    17. L. Ingber, 2011. "Computational algorithms derived from multiple scales of neocortical processing," Lester Ingber Papers 11ca, Lester Ingber.
    18. 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.
    19. Adam Gifford, 2009. "Rationality and intertemporal choice," Journal of Bioeconomics, Springer, vol. 11(3), pages 223-248, December.
    20. Hyunwoo Jang & George A. Mashour & Anthony G. Hudetz & Zirui Huang, 2024. "Measuring the dynamic balance of integration and segregation underlying consciousness, anesthesia, and sleep in humans," Nature Communications, Nature, vol. 15(1), pages 1-18, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pcbi00:1003588. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ploscompbiol (email available below). General contact details of provider: https://journals.plos.org/ploscompbiol/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.