Integrated information theory

Integrated information theory (IIT) proposes a mathematical model for the consciousness of a system. It comprises a framework ultimately intended to explain why some physical systems (such as human brains) are conscious,[1] and to be capable of providing a concrete inference about whether any physical system is conscious, to what degree, and what particular experience it is having; why they feel the particular way they do in particular states (e.g. why our visual field appears extended when we gaze out at the night sky),[2] and what it would take for other physical systems to be conscious (Are other animals conscious? Might the whole Universe be?).[3]

Phi, the symbol used for integrated information

According to IIT, a system's consciousness (what it is like subjectively) is conjectured to be identical to its causal properties (what it is like objectively). Therefore it should be possible to account for the conscious experience of a physical system by unfolding its complete causal powers (see Central identity).[4]

IIT was proposed by neuroscientist Giulio Tononi in 2004.[5] The latest version of the theory, labeled IIT 4.0 (accepted for publication after peer review in PLoS Computational biology, but see [6] for a preprint), was published in 2023, incorporating a large number of improvements to its formalism[7][2][8][9] compared to the previous version (IIT 3.0).[10][1] However, while the theory has already been applied to explain the level[4][11] and contents[2] of experience in certain situations, it remains in development and still requires further validation and testing in cases where the level and contents of experience are well known (e.g. in awake humans capable of reporting, being exposed to natural sensory stimuli).

Despite significant interest, IIT remains controversial and has been widely criticized, including that it is unfalsifiable pseudoscience.[12] Proponents counter that there is some experimental support for it, but the fundamental validity of some of the tests used is questioned by some critics.

Overview

Relationship to the "hard problem of consciousness"

David Chalmers has argued that any attempt to explain consciousness in purely physical terms (i.e. to start with the laws of physics as they are currently formulated and derive the necessary and inevitable existence of consciousness) eventually runs into the so-called "hard problem". Rather than try to start from physical principles and arrive at consciousness, IIT "starts with consciousness" (accepts the existence of our own consciousness as certain) and reasons about the properties that a postulated physical substrate would need to have in order to account for it. The ability to perform this jump from phenomenology to mechanism rests on IIT's assumption that if the formal properties of a conscious experience can be fully accounted for by an underlying physical system, then the properties of the physical system must be constrained by the properties of the experience. The limitations on the physical system for consciousness to exist are unknown and consciousness may exist on a spectrum, as implied by studies involving split brain patients[13] and conscious patients with large amounts of brain matter missing.[14]

Specifically, IIT moves from phenomenology to mechanism by attempting to identify the essential properties of conscious experience (dubbed "axioms") and, from there, the essential properties of conscious physical systems (dubbed "postulates").

Mathematics: formalization of the postulates

For a complete and thorough account of the mathematical formalization of IIT, see reference.[10] What follows is intended as a brief summary, adapted from,[15] of the most important quantities involved. Pseudocode for the algorithms used to calculate these quantities can be found at reference.[16] For a visual illustration of the algorithm, see the supplementary material of the paper describing the PyPhi toolbox.[17]

Central identity

IIT addresses the mind-body problem by proposing an identity between phenomenological properties of experience and causal properties of physical systems: The conceptual structure specified by a complex of elements in a state is identical to its experience.

Specifically, the form of the conceptual structure in cause-effect space completely specifies the quality of the experience, while the irreducibility of the conceptual structure specifies the level to which it exists (i.e., the complex's level of consciousness). The maximally irreducible cause-effect repertoire of each concept within a conceptual structure specifies what the concept contributes to the quality of the experience, while its irreducibility specifies how much the concept is present in the experience.

According to IIT, an experience is thus an intrinsic property of a complex of mechanisms in a state.

Extensions

The calculation of even a modestly-sized system's is often computationally intractable,[17] so efforts have been made to develop heuristic or proxy measures of integrated information. For example, Masafumi Oizumi and colleagues have developed both [18] and geometric integrated information or ,[19] which are practical approximations for integrated information. These are related to proxy measures developed earlier by Anil Seth and Adam Barrett.[20] However, none of these proxy measures have a mathematically proven relationship to the actual value, which complicates the interpretation of analyses that use them. They can give qualitatively different results even for very small systems.[21]

In 2021, Angus Leung and colleagues published a direct application of IIT's mathematical formalism to neural data.[22] To circumvent the computational challenges associated with larger datasets, the authors focused on neuronal population activity in the fly. The study showed that can readily be computed for smaller sets of neural data. Moreover, matching IIT's predictions, was significantly decreased when the animals underwent general anesthesia.[22]

A significant computational challenge in calculating integrated information is finding the minimum information partition of a neural system, which requires iterating through all possible network partitions. To solve this problem, Daniel Toker and Friedrich T. Sommer have shown that the spectral decomposition of the correlation matrix of a system's dynamics is a quick and robust proxy for the minimum information partition.[23]

While the algorithm[17][16] for assessing a system's and conceptual structure is relatively straightforward, its high time complexity makes it computationally intractable for many systems of interest.[17] Heuristics and approximations can sometimes be used to provide ballpark estimates of a complex system's integrated information, but precise calculations are often impossible. These computational challenges, combined with the already difficult task of reliably and accurately assessing consciousness under experimental conditions, make testing many of the theory's predictions difficult.

Despite these challenges, researchers have attempted to use measures of information integration and differentiation to assess levels of consciousness in a variety of subjects.[24][25] For instance, a recent study using a less computationally-intensive proxy for was able to reliably discriminate between varying levels of consciousness in wakeful, sleeping (dreaming vs. non-dreaming), anesthetized, and comatose (vegetative vs. minimally-conscious vs. locked-in) individuals.[26]

IIT also makes several predictions which fit well with existing experimental evidence, and can be used to explain some counterintuitive findings in consciousness research.[1] For example, IIT can be used to explain why some brain regions, such as the cerebellum do not appear to contribute to consciousness, despite their size and/or functional importance.

Reception

Integrated information theory has received both broad criticism and support.

Support

Neuroscientist Christof Koch, who has helped to develop later versions of the theory, has called IIT "the only really promising fundamental theory of consciousness".[27] Technologist and Koch's ex-student Virgil Griffith says "IIT is currently the leading theory of consciousness." However, his answer to whether IIT is exactly the right theory is 'Probably not'.[28]

Neuroscientist and consciousness researcher Anil Seth is supportive of the theory, with some caveats, claiming that "conscious experiences are highly informative and always integrated."; and that "One thing that immediately follows from [IIT] is that you have a nice post hoc explanation for certain things we know about consciousness.". But he also claims "the parts of IIT that I find less promising are where it claims that integrated information actually is consciousness — that there's an identity between the two.",[29] and has criticized the panpsychist extrapolations of the theory.[30]

Philosopher David Chalmers, famous for the idea of the hard problem of consciousness, has expressed some enthusiasm about IIT. According to Chalmers, IIT is a development in the right direction, whether or not it is correct.[31]

Physicist Max Tegmark has also expressed some support for the approach taken by IIT, and considers it compatible with his own ideas about consciousness as a "state of matter".[32] Tegmark has also tried to address the problem of the computational complexity behind the calculations. According to Max Tegmark "the integration measure proposed by IIT is computationally infeasible to evaluate for large systems, growing super-exponentially with the system's information content."[33] As a result, Φ can only be approximated in general. However, different ways of approximating Φ provide radically different results.[34] Other works have shown that Φ can be computed in some large mean-field neural network models, although some assumptions of the theory have to be revised to capture phase transitions in these large systems.[35][36]

Criticism

Influential philosopher John Searle has given a critique of theory saying "The theory implies panpsychism" and "The problem with panpsychism is not that it is false; it does not get up to the level of being false. It is strictly speaking meaningless because no clear notion has been given to the claim.".[37] However, whether or not a theory has panpsychist implications (that all or most of what exists physically must be, be part of something that is, or be composed of parts that are, conscious) has no bearing on the scientific validity of the theory. Searle's take has also been countered by other philosophers, for misunderstanding and misrepresenting a theory that is actually resonant with his own ideas.[38]

Theoretical computer scientist Scott Aaronson has criticized IIT by demonstrating through its own formulation that an inactive series of logic gates, arranged in the correct way, would not only be conscious but be "unboundedly more conscious than humans are."[39] Tononi himself agrees with the assessment and argues that according to IIT, an even simpler arrangement of inactive logic gates, if large enough, would also be conscious. However he further argues that this is a strength of IIT rather than a weakness, because that's exactly the sort of cytoarchitecture followed by large portions of the cerebral cortex,[40][41] specially at the back of the brain,[2] which is the most likely neuroanatomical correlate of consciousness according to some reviews.[42]

Philosopher Tim Bayne has criticized the axiomatic foundations of the theory.[43] He concludes that "the so-called 'axioms' that Tononi et al. appeal to fail to qualify as genuine axioms".

The mathematics of IIT have also been criticized since "having a high Φ value requires highly specific structures that are unstable to minor perturbations".[44] This susceptibility to minor perturbations seems inconsistent with empirical results about neuroplasticity in the human brain, and thus weakening the theory. However, the systems investigated by Schwitzgebel were small networks of logic gates, and not human brains in normal waking conditions, and the generalizability to systems about which we have access to verified conscious experience (human beings) is questionable.

A peer-reviewed commentary by 58 scholars involved in the scientific study of consciousness rejects these conclusions about logic gates as "mysterious and unfalsifiable claims" that should be distinguished from "empirically productive hypotheses".[45] IIT as a scientific theory of consciousness has been criticized in the scientific literature as only able to be "either false or unscientific" by its own definitions.[46] IIT has also been denounced by other members of the consciousness field as requiring "an unscientific leap of faith", but it is not clear that this is in fact the case if the theory is properly understood.[47] The theory has also been derided for failing to answer the basic questions required of a theory of consciousness. Philosopher Adam Pautz says "As long as proponents of IIT do not address these questions, they have not put a clear theory on the table that can be evaluated as true or false."[48] Neuroscientist Michael Graziano, proponent of the competing attention schema theory, rejects IIT as pseudoscience. He claims IIT is a "magicalist theory" that has "no chance of scientific success or understanding".[49] Similarily, IIT was criticized that its claims are "not scientifically established or testable at the moment".[50]

However, while it is true that the complete analysis suggested by IIT cannot be completed at the moment for human brains, IIT has already been applied to models of visual cortex to explain why visual space feels the way it does.[2]

Neuroscientists Björn Merker, David Rudrauf and Philosopher Kenneth Williford co-authored a paper criticizing IIT on several grounds. Firstly, by not demonstrating that all members of systems which do in fact combine integration and differentiation in the formal IIT sense are conscious, systems which demonstrate high levels of integration and differentiation of information might provide the necessary conditions for consciousness but those combinations of attributes do not amount to the conditions for consciousness. Secondly that the measure, Φ, reflects efficiency of global information transfer rather than level of consciousness, and that the correlation of Φ with level of consciousness through different states of wakefulness (e.g. awake, dreaming and dreamless sleep, anesthesia, seizures and coma) actually reflect the level of efficient network interactions performed for cortical engagement. Hence Φ reflects network efficiency rather than consciousness, which would be one of the functions served by cortical network efficiency.[51]

A letter published on 15 September 2023 in the preprint repository PsyArXiv and signed by 124 scholars asserted that until IIT is empirically testable, it should be labeled pseudoscience.[52] A number of researchers defended the theory in response.[12] Regarding this letter, IIT, and what he considers a similarly unscientific theory, Assembly theory (AT), University of Cambridge and University of Oxford computer scientist Hector Zenil made criticisms based on the lack of correspondence of the methods and theory in some of the IIT research papers and the media frenzy.[53] Zenil criticized both the shallowness and misleading nature of the media coverage, including in apparently respected journals such as Nature and Science. He also criticized testing methods and evidence used by IIT proponents, noting that one test amounted to simply applying LZW compression to measure entropy rather than to indicate consciousness as proponents claimed.

Adversarial Collaboration to test GNW and IIT

In 2019, the Templeton Foundation announced funding in excess of $6,000,000 to test opposing empirical predictions of IIT and a rival theory (Global Neuronal Workspace Theory GNWT).[54][55] The originators of both theories signed off on experimental protocols and data analyses as well as the exact conditions that satisfy if their championed theory correctly predicted the outcome or not.[56][57] Initial results were revealed in June 2023.[58] None of GNWT's predictions passed what was agreed upon pre-registration while two out of three of IIT's predictions passed that threshold.[59]

See also

References
  1. Tononi, Giulio (2015). "Integrated information theory". Scholarpedia. 10 (1): 4164. Bibcode:2015SchpJ..10.4164T. doi:10.4249/scholarpedia.4164.
  2. Haun, Andrew; Tononi, Giulio (December 2019). "Why Does Space Feel the Way it Does? Towards a Principled Account of Spatial Experience". Entropy. 21 (12): 1160. Bibcode:2019Entrp..21.1160H. doi:10.3390/e21121160. PMC 7514505.
  3. Tononi, Giulio; Koch, Christof (19 May 2015). "Consciousness: here, there and everywhere?". Philosophical Transactions of the Royal Society B: Biological Sciences. 370 (1668): 20140167. doi:10.1098/rstb.2014.0167. ISSN 0962-8436. PMC 4387509. PMID 25823865.
  4. Tononi, Giulio; Boly, Melanie; Massimini, Marcello; Koch, Christof (2016). "Integrated information theory: from consciousness to its physical substrate". Nature Reviews Neuroscience. 17 (7): 450–461. doi:10.1038/nrn.2016.44. PMID 27225071. S2CID 21347087.
  5. Tononi, Giulio (2 November 2004). "An information integration theory of consciousness". BMC Neuroscience. 5 (1): 42. doi:10.1186/1471-2202-5-42. ISSN 1471-2202. PMC 543470. PMID 15522121.
  6. Albantakis, Larissa (20 September 2023). "Integrated information theory (IIT) 4.0: Formulating the properties of phenomenal existence in physical terms". arXiv:2212.14787 [q-bio.NC].
  7. Barbosa, Leonardo S.; Marshall, William; Streipert, Sabrina; Albantakis, Larissa; Tononi, Giulio (2 November 2020). "A measure for intrinsic information". Scientific Reports. 10 (1): 18803. Bibcode:2020NatSR..1018803B. doi:10.1038/s41598-020-75943-4. ISSN 2045-2322. PMC 7606539. PMID 33139829.
  8. Barbosa, Leonardo S.; Marshall, William; Albantakis, Larissa; Tononi, Giulio (March 2021). "Mechanism Integrated Information". Entropy. 23 (3): 362. Bibcode:2021Entrp..23..362B. doi:10.3390/e23030362. PMC 8003304. PMID 33803765.
  9. Marshall, William; Albantakis, Larissa; Tononi, Giulio (23 April 2018). Schrater, Paul (ed.). "Black-boxing and cause-effect power". PLOS Computational Biology. 14 (4): e1006114. arXiv:1608.03461. Bibcode:2018PLSCB..14E6114M. doi:10.1371/journal.pcbi.1006114. ISSN 1553-7358. PMC 5933815. PMID 29684020.
  10. Oizumi, Masafumi; Albantakis, Larissa; Tononi, Giulio (8 May 2014). "From the Phenomenology to the Mechanisms of Consciousness: Integrated Information Theory 3.0". PLOS Comput Biol. 10 (5): e1003588. Bibcode:2014PLSCB..10E3588O. doi:10.1371/journal.pcbi.1003588. PMC 4014402. PMID 24811198.
  11. Casarotto, Silvia; Comanducci, Angela; Rosanova, Mario; Sarasso, Simone; Fecchio, Matteo; Napolitani, Martino; Pigorini, Andrea; G. Casali, Adenauer; Trimarchi, Pietro D.; Boly, Melanie; Gosseries, Olivia; Bodart, Olivier; Curto, Francesco; Landi, Cristina; Mariotti, Maurizio (November 2016). "Stratification of unresponsive patients by an independently validated index of brain complexity". Annals of Neurology. 80 (5): 718–729. doi:10.1002/ana.24779. ISSN 0364-5134. PMC 5132045. PMID 27717082.
  12. Mariana Lenharo (20 September 2023). "Consciousness theory slammed as 'pseudoscience' — sparking uproar". Nature.
  13. De Haan, Edward H. F.; Corballis, Paul M.; Hillyard, Steven A.; Marzi, Carlo A.; Seth, Anil; Lamme, Victor A. F.; Volz, Lukas; Fabri, Mara; Schechter, Elizabeth; Bayne, Tim; Corballis, Michael; Pinto, Yair (2020). "Split-Brain: What We Know Now and Why This is Important for Understanding Consciousness". Neuropsychology Review. 30 (2): 224–233. doi:10.1007/s11065-020-09439-3. PMC 7305066. PMID 32399946. S2CID 216440326.
  14. Feuillet, Lionel; Dufour, Henry; Pelletier, Jean (2007). "Brain of a white-collar worker". The Lancet. 370 (9583): 262. doi:10.1016/S0140-6736(07)61127-1. PMID 17658396. S2CID 7382008.
  15. Albantakis, Larissa; Tononi, Giulio (31 July 2015). "The Intrinsic Cause-Effect Power of Discrete Dynamical Systems—From Elementary Cellular Automata to Adapting Animats". Entropy. 17 (8): 5472–5502. Bibcode:2015Entrp..17.5472A. doi:10.3390/e17085472.
  16. "CSC-UW/iit-pseudocode". GitHub. Retrieved 29 January 2016.
  17. Mayner, William G. P.; Marshall, William; Albantakis, Larissa; Findlay, Graham; Marchman, Robert; Tononi, Giulio (26 July 2018). "PyPhi: A toolbox for integrated information theory". PLOS Computational Biology. 14 (7): e1006343. arXiv:1712.09644. Bibcode:2018PLSCB..14E6343M. doi:10.1371/journal.pcbi.1006343. ISSN 1553-7358. PMC 6080800. PMID 30048445.
  18. Oizumi, Masafumi; Amari, Shun-ichi; Yanagawa, Toru; Fujii, Naotaka; Tsuchiya, Naotsugu (17 May 2015). "Measuring integrated information from the decoding perspective". PLOS Computational Biology. 12 (1): e1004654. arXiv:1505.04368. Bibcode:2016PLSCB..12E4654O. doi:10.1371/journal.pcbi.1004654. PMC 4721632. PMID 26796119.
  19. Oizumi, Masafumi; Tsuchiya, Naotsugu; Amari, Shun-ichi (20 December 2016). "Unified framework for information integration based on information geometry". Proceedings of the National Academy of Sciences. 113 (51): 14817–14822. arXiv:1510.04455. Bibcode:2016PNAS..11314817O. doi:10.1073/pnas.1603583113. PMC 5187746. PMID 27930289.
  20. Barrett, A.B.; Seth, A.K. (2011). "Practical measures of integrated information for time-series data". PLOS Comput. Biol. 7 (1): e1001052. Bibcode:2011PLSCB...7E1052B. doi:10.1371/journal.pcbi.1001052. PMC 3024259. PMID 21283779.
  21. Mediano, Pedro; Seth, Anil; Barrett, Adam (25 December 2018). "Measuring Integrated Information: Comparison of Candidate Measures in Theory and Simulation". Entropy. 21 (1): 17. arXiv:1806.09373. Bibcode:2018Entrp..21...17M. doi:10.3390/e21010017. ISSN 1099-4300. PMC 7514120. PMID 33266733.
  22. Leung, Angus; Cohen, Dror; van Swinderen, Bruno; Tsuchiya, Naotsugu (2021). "Integrated information structure collapses with anesthetic loss of conscious arousal in Drosophila melanogaster". PLOS Computational Biology. 17 (2): e1008722. Bibcode:2021PLSCB..17E8722L. doi:10.1371/journal.pcbi.1008722. ISSN 1553-7358. PMC 7946294. PMID 33635858.
  23. Toker, Daniel; Sommer, Friedrich T.; Marinazzo, Daniele (7 February 2019). "Information integration in large brain networks". PLOS Computational Biology. 15 (2): e1006807. Bibcode:2019PLSCB..15E6807T. doi:10.1371/journal.pcbi.1006807. PMC 6382174. PMID 30730907.
  24. Massimini, M.; Ferrarelli, F.; Murphy, Mj; Huber, R.; Riedner, Ba; Casarotto, S.; Tononi, G. (1 September 2010). "Cortical reactivity and effective connectivity during REM sleep in humans". Cognitive Neuroscience. 1 (3): 176–183. doi:10.1080/17588921003731578. ISSN 1758-8936. PMC 2930263. PMID 20823938.
  25. Ferrarelli, Fabio; Massimini, Marcello; Sarasso, Simone; Casali, Adenauer; Riedner, Brady A.; Angelini, Giuditta; Tononi, Giulio; Pearce, Robert A. (9 February 2010). "Breakdown in cortical effective connectivity during midazolam-induced loss of consciousness". Proceedings of the National Academy of Sciences of the United States of America. 107 (6): 2681–2686. Bibcode:2010PNAS..107.2681F. doi:10.1073/pnas.0913008107. ISSN 1091-6490. PMC 2823915. PMID 20133802.
  26. Casali, Adenauer G.; Gosseries, Olivia; Rosanova, Mario; Boly, Mélanie; Sarasso, Simone; Casali, Karina R.; Casarotto, Silvia; Bruno, Marie-Aurélie; Laureys, Steven; Massimini, Marcello (14 August 2013). "A Theoretically Based Index of Consciousness Independent of Sensory Processing and Behavior". Science Translational Medicine. 5 (198): 198ra105. doi:10.1126/scitranslmed.3006294. hdl:2268/171542. ISSN 1946-6234. PMID 23946194. S2CID 8686961.
  27. Zimmer, Carl (20 September 2010). "Sizing Up Consciousness by Its Bits". The New York Times. ISSN 0362-4331. Retrieved 23 November 2015.
  28. "How valid is Giulio Tononi's mathematical formula for consciousness?".
  29. Falk, Dan (30 September 2021). "Anil Seth Finds Consciousness in Life's Push Against Entropy". Quanta Magazine. Retrieved 16 December 2021.
  30. akseth (1 February 2018). "Conscious spoons, really? Pushing back against panpsychism". NeuroBanter. Retrieved 16 December 2021.
  31. Chalmers, David (14 July 2014), How do you explain consciousness?, retrieved 16 December 2021
  32. Consciousness is a mathematical pattern: Max Tegmark at TEDxCambridge 2014, retrieved 30 May 2022
  33. Tegmark, Max (2016). "Improved Measures of Integrated Information". PLOS Computational Biology. 12 (11): e1005123. arXiv:1601.02626. Bibcode:2016PLSCB..12E5123T. doi:10.1371/journal.pcbi.1005123. PMC 5117999. PMID 27870846.
  34. Mediano, Pedro; Seth, Anil; Barrett, Adam (2019). "Measuring Integrated Information: Comparison of Candidate Measures in Theory and Simulation". Entropy. 21 (1): 17. arXiv:1806.09373. Bibcode:2018Entrp..21...17M. doi:10.3390/e21010017. PMC 7514120. PMID 33266733.
  35. Aguilera, Miguel; Di Paolo, Ezequiel (2019). "Integrated information in the thermodynamic limit". Neural Networks. 114: 136–146. doi:10.1016/j.neunet.2019.03.001. PMID 30903946.
  36. Aguilera, Miguel (2019). "Scaling Behaviour and Critical Phase Transitions in Integrated Information Theory". Entropy. 21 (12): 1198. Bibcode:2019Entrp..21.1198A. doi:10.3390/e21121198. PMC 7514544.
  37. Searle, John. "Can Information Theory Explain Consciousness?". The New York Review of Books.
  38. Fallon, Francis (1 September 2020). "Integrated Information Theory, Searle, and the Arbitrariness Question". Review of Philosophy and Psychology. 11 (3): 629–645. doi:10.1007/s13164-018-0409-0. ISSN 1878-5166. S2CID 149881007.
  39. Aaronson, Scott (21 May 2014). "Why I Am Not An Integrated Information Theorist (or, The Unconscious Expander)". Shetl-Optimized: The Blog of Scott Aaronson.
  40. Aaronson, Scott (30 May 2014). "Giulio Tononi and Me: A Phi-nal Exchange". Shetl-Optimized: The Blog of Scott Aaronson.
  41. Tononi, Giulio. "Why Scott should stare at a blank wall and reconsider (or, the conscious grid)". Shetl-Optimized: The Blog of Scott Aaronson.
  42. Boly, Melanie; Massimini, Marcello; Tsuchiya, Naotsugu; Postle, Bradley R.; Koch, Christof; Tononi, Giulio (4 October 2017). "Are the Neural Correlates of Consciousness in the Front or in the Back of the Cerebral Cortex? Clinical and Neuroimaging Evidence". Journal of Neuroscience. 37 (40): 9603–9613. doi:10.1523/JNEUROSCI.3218-16.2017. ISSN 0270-6474. PMC 5628406. PMID 28978697. S2CID 41370141.
  43. Bayne, Tim (2018). "On the axiomatic foundations of the integrated information theory of consciousness". Neuroscience of Consciousness. 2018 (1): niy007. doi:10.1093/nc/niy007. PMC 6030813. PMID 30042860.
  44. Schwitzgebel, Eric (9 November 2018). "The Phi Value of Integrated Information Theory Might Not Be Stable Across Small Changes in Neural Connectivity". The Splintered Mind: Reflections in Philosophy of Psychology, Broadly Contrued.
  45. Michel, Matthias; Beck, Diane; Block, Ned; Blumenfeld, Hal; Brown, Richard; Carmel, David; Carrasco, Marisa; Chirimuuta, Mazviita; Chun, Marvin; Cleeremans, Axel; Dehaene, Stanislas; Fleming, Stephen; Frith, Chris; Haggard, Patrick; He, Biyu; Heyes, Cecilia; Goodale, Mel; Irvine, Liz; Kawato, Mitsuo; Kentridge, Robert; King, JR; Knight, Robert; Kouider, Sid; Lamme, Victor; Lamy, Dominique; Lau, Hakwan; Laureys, Steven; LeDoux, Joseph; Lin, Ying-Tung; Liu, Kayuet; Macknik, Stephen; Martinez-Conde, Susana; Mashour, George; Melloni, Lucia; Miracchi, Lisa; Mylopoulos, Myrto; Naccache, Lionel; Owen, Adrian; Passingham, Richard; Pessoa, Luiz; Peters, Megan; Rahnev, Dobromir; Ro, Tony; Rosenthal, David; Sasaki, Yuka; Sergent, Claire; Solovey, Guillermo; Schiff, Nicholas; Seth, Anil; Tallon-Baudry, Catherine; Tamietto, Marco; Tong, Frank; van Gaal, Simon; Vlassova, Alexandra; Watanabe, Takeo; Weisberg, Josh; Yan, Karen; Yoshida, Masatoshi (4 February 2019). "Opportunities and challenges for a maturing science of consciousness". Nature Human Behaviour. 3 (2): 104–107. doi:10.1038/s41562-019-0531-8. PMC 6568255. PMID 30944453.
  46. Doerig, Adrian; Schruger, Aaron; Hess, Kathryn; Herzog, Michael (2019). "The unfolding argument: Why IIT and other causal structure theories cannot explain consciousness". Consciousness and Cognition. 72: 49–59. doi:10.1016/j.concog.2019.04.002. PMID 31078047.
  47. Lau, Hakwan; Michel, Matthias (2019). "On the dangers of conflating strong and weak versions of a theory of consciousness". PsyArXiv. doi:10.31234/osf.io/hjp3s. S2CID 203047817.
  48. Pautz, Adam (2019). "What is Integrated Information Theory?: A Catalogue of Questions". Journal of Consciousness Studies. 26` (1): 188–215.
  49. Jarrett, Christian (5 April 2020). "Consciousness: how can we solve the greatest mystery in science?". BBC Science Focus Magazine. Retrieved 2 February 2021.
  50. au, Hakwan (28 May 2020). "Open letter to NIH on Neuroethics Roadmap (BRAIN initiative) 2019". In Consciousness We Trust..
  51. Merker, Björn (19 May 2021). "The Integrated Information Theory of consciousness: A case of mistaken identity". Behavioral and Brain Sciences. 45: e41. doi:10.1017/S0140525X21000881. PMID 34006338.
  52. Fleming, S. M.; Frith, C.; Goodale, M.; Lau, H.; LeDoux, J. E.; Lee, A. L. F.; et al. (2023). "The Integrated Information Theory of Consciousness as Pseudoscience". PsyArXiv (Preprint). doi:10.31234/osf.io/zsr78. Retrieved 18 September 2023.
  53. Zenil, Hector. "How Some Scientific Journalism is Failing Us, to both Readers & (less self-promotional) Scientists – Medium". Retrieved 27 September 2023.
  54. "Accelerating Research on Consciousness: An Adversarial Collaboration to Test Contradictory Predictions of Global Neuronal Workspace and Integrated Information Theory". www.templetonworldcharity.org. Retrieved 16 July 2023.
  55. Melloni, Lucia; Mudrik, Liad; Pitts, Michael; Koch, Christof (28 May 2021). "Making the hard problem of consciousness easier". Science. 372 (6545): 911–912. doi:10.1126/science.abj3259. ISSN 0036-8075. PMID 34045342. S2CID 235218711.
  56. Melloni, Lucia; Mudrik, Liad; Pitts, Michael; Blumenfeld, Hal; Lange, Floris de; Jensen, Ole; Kreiman, Gabriel; Luo, Huan; Chalmers, David; Dehaene, Stanislas (16 January 2019). "Adversarial Collaboration to test GNW and IIT". Center for Open Science.
  57. Melloni, Lucia; Mudrik, Liad; Pitts, Michael; Bendtz, Katarina; Ferrante, Oscar; Gorska, Urszula; Hirschhorn, Rony; Khalaf, Aya; Kozma, Csaba; Lepauvre, Alex; Liu, Ling; Mazumder, David; Richter, David; Zhou, Hao; Blumenfeld, Hal (10 February 2023). "An adversarial collaboration protocol for testing contrasting predictions of global neuronal workspace and integrated information theory". PLOS ONE. 18 (2): e0268577. Bibcode:2023PLoSO..1868577M. doi:10.1371/journal.pone.0268577. ISSN 1932-6203. PMC 9916582. PMID 36763595.
  58. Finkel, Elizabeth (30 June 2023). "Consciousness hunt yields results but not clarity". Science. 380 (6652): 1309–1310. Bibcode:2023Sci...380.1309F. doi:10.1126/science.adj4498. ISSN 0036-8075. PMID 37384689. S2CID 259287604.
  59. Ferrante, O.; et al. (Cogitate Consortium) (26 June 2023). "An adversarial collaboration to critically evaluate theories of consciousness". bioRxiv 10.1101/2023.06.23.546249.
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