Qualia: The Geometry of Integrated Information
Open accessAbstract
According to the integrated information theory, the quantity of consciousness is the amount of integrated information generated by a complex of elements, and the quality of experience is specified by the informational relationships it generates. This paper outlines a framework for characterizing the informational relationships generated by such systems. Qualia space (Q) is a space having an axis for each possible state (activity pattern) of a complex. Within Q, each submechanism specifies a point corresponding to a repertoire of system states. Arrows between repertoires in Q define informational relationships. Together, these arrows specify a quale -- a shape that completely and univocally characterizes the quality of a conscious experience. Phi -- the height of this shape -- is the quantity of consciousness associated with the experience. Entanglement measures how irreducible informational relationships are to their component relationships, specifying concepts and modes. Several corollaries follow from these premises. The quale is determined by both the mechanism and state of the system. Thus, two different systems having identical activity patterns may generate different qualia. Conversely, the same quale may be generated by two systems that differ in both activity and connectivity. Both active and inactive elements specify a quale, but elements that are inactivated do not. Also, the activation of an element affects experience by changing the shape of the quale. The subdivision of experience into modalities and submodalities corresponds to subshapes in Q. In principle, different aspects of experience may be classified as different shapes in Q, and the similarity between experiences reduces to similarities between shapes. Finally, specific qualities, such as the "redness" of red, while generated by a local mechanism, cannot be reduced to it, but require considering the entire quale. Ultimately, the present framework may offer a principled way for translating qualitative properties of experience into mathematics.
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IIT offers an explanation for why intelligence and consciousness may tend to co-evolve in biological organisms: selective pressure for intelligence, acting on resource-limited substrates such as brains, typically leads to an increase in integration among the parts of a system.
“We showed previously that IIT offers an explanation for why intelligence and consciousness may tend to co-evolve in biological organisms: selective pressure for intelligence, acting on resource-limited substrates such as brains, typically leads to an increase in integration among the parts of a system [94,95].”
From Dissociating Artificial Intelligence from Artificial Consciousness (2024) · cites this paper as [95]
Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredIIT appears to be identifying consciousness with a specific measure of information.
“In either case, IIT appears to be identifying consciousness with a specific measure of information.”
From The Problem with Phi: A Critique of Integrated Information Theory (2015) · cites this paper as [5]
The Introduction says IIT identifies the quantity of consciousness with the amount of integrated information and describes Φ as a measure of it. But it says the quality of consciousness is specified by informational relationships, not by that single measure, so the claim is too broad if it refers to consciousness as a whole. (LLM, conf=0.84)
Experimental — accuracy not yet measuredIIT postulates that consciousness is equal to integrated information (Φ).
“IIT defines a property of a system called integrated information (Φ) and postulates that this is an exact measure of the quantity of consciousness of any system.”
From The Problem with Phi: A Critique of Integrated Information Theory (2015) · cites this paper as [5]
IIT implies a form of panpsychism.
“Tononi’s response is not surprising given that IIT implies a form of panpsychism [1–8].”
From The Problem with Phi: A Critique of Integrated Information Theory (2015) · cites this paper as [5]
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Cites
Information theory and statistical mechanics
Jaynes, E. T. (1957) · Physical Review · doi:10.1103/physrev.106.620 · Cited by 12,931 · see citing works
Causality: Models, Reasoning, and Inference
Pearl, J. (2009) · doi:10.1017/cbo9780511803161 · Cited by 11,653 · see citing works
Distributed hierarchical processing in the primate cerebral cortex
Felleman, D. J., & Van Essen, D. C. (1991) · Cerebral Cortex · doi:10.1093/cercor/1.1.1 · Cited by 7,783 · see citing works
Attention regulation and monitoring in meditation
Lutz, A., Slagter, H. A., Dunne, J. D., & Davidson, R. J. (2008) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2008.01.005 · Cited by 2,659 · see citing works
Towards a cognitive neuroscience of consciousness: basic evidence and a workspace framework
Dehaene, S. (2001) · Cognition · doi:10.1016/s0010-0277(00)00123-2 · Cited by 2,498 · cited 1× · see citing works
“relevant information to a large public of other neurons [23].”
BackgroundAn information integration theory of consciousness
Tononi, G. (2004) · BMC Neuroscience · doi:10.1186/1471-2202-5-42 · Cited by 1,757 · cited 31× · see citing works
“A measure of integrated information, called phi (W), can be used to quantify the information generated when a system is in a particular state of its repertoire, above and beyond the information generated independently by its parts [1].”
BackgroundInfluentialSelf-citationHierarchical Bayesian inference in the visual cortex
Lee, T. S., & Mumford, D. (2003) · Journal of the Optical Society of America A · doi:10.1364/josaa.20.001434 · Cited by 1,473 · cited 1× · see citing works
“In future work, we will relate the concepts generated by a system to Bayesian inference [27–29].”
MethodHierarchical Models in the Brain
Friston, K. (2008) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000211 · Cited by 952 · cited 1× · see citing works
“Much of experience is hierarchically organized [35,36] and, perhaps not coincidentally, so is the organization of sensory pathways in the cortex [37–39].”
BackgroundConsciousness, personal identity and the divided brain
Sperry, R. (1984) · Neuropsychologia · doi:10.1016/0028-3932(84)90093-9 · Cited by 231 · cited 1× · see citing works
“Particularly surprising is that the dominant (verbal) hemisphere appears to behave similarly to an intact brain, and reports largely similar experiences [44].”
BackgroundDynamic causal modelling of induced responses
Chen, C. C., Kiebel, S. J., & Friston, K. J. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2008.03.026 · Cited by 136 · see citing works
Flagged first, then by citations
Research works printed but not linked: 33
- [2]Sperry RW 1961 Cerebral Organization and Behavior: The split brain behaves in many respects like two separate brains, providing new research possibilities. Science 133 1749 1757 17829720
- [3]Balduzzi D Tononi G 2008 Integrated information in discrete dynamical systems: motivation and theoretical framework. PLoS Comput Biol 4 e1000091 18551165 PMC2386970
- [7]Davey B Priestly H 2002 Introduction to Lattices and Order. Cambridge University Press
- [8]Stanley RP 2000 Enumerative Combinatorics. Volume I. Cambridge University Press
- [12]Garcia-Carpintero M Macia J 2006 Two-Dimensional Semantics. Oxford University Press
- [13]Pearl J 1988 Probabilistic reasoning in intelligent systems: networks of plausible inference. Morgan Kaufmann Publishers
- [15]Lauritzen S 1996 Graphical Models. Oxford University Press
- [16]Nakahara H Amari S 2002 Information-geometric measure for neural spikes. Neural Comput 14 2269 2316 12396564
- [17]Amari S 2001 Information geometry on hierarchy of probability distributions. IEEE Trans Inf Theory 47 1701 1711
- [18]Ay N Olbrich E Bertschinger N Jost J 2006 A unifying framework for complexity measures of finite systems. Proceedings of ECCS06 Oxford, UK European Complex Systems Society
- [20]McIntosh AR Rajah MN Lobaugh NJ 2003 Functional connectivity of the medial temporal lobe relates to learning and awareness. J Neurosci 23 6520 6528 12878693 PMC6740619
- [21]McIntosh AR 2000 Towards a network theory of cognition. Neural Netw 13 861 870 11156197
- [22]McIntosh AR 2004 Contexts and catalysts: a resolution of the localization and integration of function in the brain. Neuroinformatics 2 175 182 15319515
- [24]Crick F Koch C 2003 A framework for consciousness. Nat Neurosci 6 119 126 12555104
- [26]Quiroga RQ Reddy L Kreiman G Koch C Fried I 2005 Invariant visual representation by single neurons in the human brain. Nature 435 1102 1107 15973409
- [28]Tenenbaum JB Griffiths TL 2001 Generalization, similarity, and Bayesian inference. Behav Brain Sci 24 629 640; discussion 652–791 12048947
- [29]Kording KP Wolpert DM 2006 Bayesian decision theory in sensorimotor control. Trends Cogn Sci 10 319 326 16807063
- [30]Friston K Kilner J Harrison L 2006 A free energy principle for the brain. J Physiol Paris 100 70 87 17097864
- [31]Tononi G Sporns O Edelman GM 1996 A complexity measure for selective matching of signals by the brain. Proc Natl Acad Sci U S A 93 3422 3427 8622951 PMC39624
- [32]Friston K 2003 Learning and inference in the brain. Neural Netw 16 1325 1352 14622888
- [33]Lungarella M Sporns O 2006 Mapping information flow in sensorimotor networks. PLoS Comput Biol 2 e144 17069456 PMC1626158
- [34]Livingstone MS Hubel DH 1984 Anatomy and physiology of a color system in the primate visual cortex. J Neurosci 4 309 356 6198495 PMC6564760
- [36]Humphreys GW Forde EM 2001 Hierarchies, similarity, and interactivity in object recognition: “category-specific” neuropsychological deficits. Behav Brain Sci 24 453 476; discussion 476–509 11682799
- [38]Riesenhuber M Poggio T 1999 Hierarchical models of object recognition in cortex. Nat Neurosci 2 1019 1025 10526343
- [40]Attneave F 1954 Some informational aspects of visual perception. Psychol Rev 61 183 193 13167245
- [43]Edelman S 1998 Representation is representation of similarities. Behav Brain Sci 21 449 467; discussion 467–498 10097019
- [45]Tononi G Sporns O Edelman GM 1999 Measures of degeneracy and redundancy in biological networks. Proc Natl Acad Sci U S A 96 3257 3262 10077671 PMC15929
- [46]Driver J Vuilleumier P 2001 Perceptual awareness and its loss in unilateral neglect and extinction. Cognition 79 39 88 11164023
- [47]Crick F Koch C 2003 A framework for consciousness. Nat Neurosci 6 119 126 12555104
- [48]van Zandvoort MJ Nijboer TC de Haan E 2007 Developmental colour agnosia. Cortex 43 750 757 17710826
- [49]Posner JB Plum F 2007 Plum and Posner's diagnosis of stupor and coma Oxford, New York Oxford University Press xiv, 401
- [50]Friston KJ 2005 Models of brain function in neuroimaging. Annu Rev Psychol 56 57 87 15709929
- [52]Friston KJ Harrison L Penny W 2003 Dynamic causal modelling. Neuroimage 19 1273 1302 12948688
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Oizumi, M., Albantakis, L., & Tononi, G. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003588 · Cited by 1,128 · see citing works
Self-citationConsciousness: here, there and everywhere?
Tononi, G., & Koch, C. (2015) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2014.0167 · Cited by 811 · see citing works
Self-citationNo-Report Paradigms: Extracting the True Neural Correlates of Consciousness
Tsuchiya, N., Wilke, M., Frässle, S., & Lamme, V. A. F. (2015) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2015.10.002 · Cited by 551 · see citing works
Albantakis, L., Barbosa, L., Findlay, G., Grasso, M., Haun, A. M., Marshall, W., Mayner, W. G. P., Zaeemzadeh, A., Boly, M., Juel, B. E., et al. (2023) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1011465 · Cited by 292 · see citing works
Self-citationPractical Measures of Integrated Information for Time-Series Data
Barrett, A. B., & Seth, A. K. (2011) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1001052 · Cited by 225 · see citing works
Measuring Integrated Information from the Decoding Perspective
Oizumi, M., Amari, S.-I., Yanagawa, T., Fujii, N., & Tsuchiya, N. (2016) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1004654 · Cited by 184 · see citing works
Sentience and the Origins of Consciousness: From Cartesian Duality to Markovian Monism
Friston, K. J., Wiese, W., & Hobson, J. A. (2020) · Entropy · doi:10.3390/e22050516 · Cited by 186 · see citing works
Integrating information in the brain's EM field: the cemi field theory of consciousness
McFadden, J. (2020) · Neuroscience of Consciousness · doi:10.1093/nc/niaa016 · Cited by 148 · see citing works
The unfolding argument: Why IIT and other causal structure theories cannot explain consciousness
Doerig, A., Schurger, A., Hess, K., & Herzog, M. H. (2019) · Consciousness and Cognition · doi:10.1016/j.concog.2019.04.002 · Cited by 140 · see citing works
Safron, A. (2020) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2020.00030 · Cited by 133 · see citing works
“The self-directed causal relation of a maximal complex is referred to as a maximally irreducible conceptual structure (MICS) — corresponding to mappings onto an abstract metric space (i.e., “qualia space”) (Balduzzi and Tononi, 2009), whose particular geometries correspond to particular experiences.”
BackgroundThe Problem with Phi: A Critique of Integrated Information Theory
Cerullo, M. A. (2015) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1004286 · Cited by 114 · see citing works
“While the details of the mathematical calculation of integrated information have varied with each new version of IIT, this abstract definition of integrated information has remained unchanged [1–8].”
BackgroundResultInfluentialAlbantakis, L., Hintze, A., Koch, C., Adami, C., & Tononi, G. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003966 · Cited by 101 · see citing works
Conscious perception as integrated information patterns in human electrocorticography
Haun, A. M., Oizumi, M., Kovach, C. K., Kawasaki, H., Oya, H., Howard, M. A., Adolphs, R., & Tsuchiya, N. (2017) · eneuro · doi:10.1523/eneuro.0085-17.2017 · Cited by 47 · see citing works
Causal composition: Structural differences among dynamically equivalent systems
Albantakis, L., & Tononi, G. (2019) · Entropy · doi:10.3390/e21100989 · Cited by 30 · see citing works
“…integrated information measure proposed in [56], but rather by min While the role of composition in accounting for the quality of phenomenal experience (see Section 4.3) had already been recognized in earlier publications [57], it was not incorporated in the quantitative measure φ 2.0 [58].”
BackgroundInfluentialSelf-citationDynamical Emergence Theory (DET): A Computational Account of Phenomenal Consciousness
Moyal, R., Fekete, T., & Edelman, S. (2020) · Minds and Machines · doi:10.1007/s11023-020-09516-9 · Cited by 26 · see citing works
A measure centrality index for systematic empirical comparison of consciousness theories
Chis-Ciure, R., Melloni, L., & Northoff, G. (2024) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2024.105670 · Cited by 22 · see citing works
Negro, N. (2024) · Neuroscience of Consciousness · doi:10.1093/nc/niae012 · Cited by 21 · see citing works
Evers, K., Farisco, M., & Pennartz, C. M. A. (2024) · Consciousness and Cognition · doi:10.1016/j.concog.2024.103668 · Cited by 22 · see citing works
First-person experience cannot rescue causal structure theories from the unfolding argument
Herzog, M. H., Schurger, A., & Doerig, A. (2022) · Consciousness and Cognition · doi:10.1016/j.concog.2021.103261 · Cited by 14 · see citing works
Dissociating Artificial Intelligence from Artificial Consciousness
Findlay, G., Marshall, W., Albantakis, L., David, I., Mayner, W. G., Koch, C., & Tononi, G. (2024) · arXiv · doi:10.48550/arxiv.2412.04571 · Cited by 6 · see citing works
“…consciousness may split under some concurrent tasks [29], why specific contents of consciousness such as the visual field, temporal flow or mental objects may feel the way they do [40, 41, 91–93], and why consciousness may have evolved due to selective pressure for information integration [94–96].”
Self-citationCorcoran, A. W., Haun, A. M., Dorman, R., Tononi, G., Friston, K. J., & Pennartz, C. M. A. (2026) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2026.106742 · Cited by 2 · see citing works
Self-citation
- Authors
- Balduzzi, D., & Tononi, G.
- Affiliations
- University of Wisconsin–Madison (US)
- Corresponding author
- Tononi, Giulio
- Venue
- PLoS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2009-08-14
- Volume
- 5
- Issue
- 8
- Pages
- e1000462
- ISSN
- 1553-734X (ISSN-L)
- 1553-7358
- PMC
- PMC2713405
- OpenAlex
- W2120745192
- Cited by
- 269
- References
- 10
- Citations by year
- 2026: 13 · 2025: 15 · 2024: 13 · 2023: 10 · 2022: 17 · 2021: 21 · 2020: 14 · 2019: 31 · 2018: 22 · 2017: 10 · 2016: 14 · 2015: 15 · 2014: 21 · 2013: 11 · 2012: 12
- FWCI
- 17.77
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PLoS Computational Biology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- Funders
- James S. McDonnell Foundation
- National Institutes of Health (DP1OD000579)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Philosophy and Theoretical Science
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Cognitive Neuroscience › Embodied and Extended Cognition
- Keywords
- qualia, integrated information theory, consciousness, entanglement, quality of experience
- MeSH terms
- Algorithms, Artificial Intelligence, Consciousness, Information Theory, Models, Theoretical, Databases, Factual, Computational Biology
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