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Zhang Y. A Categorical Model of General Consciousness. Biomimetics (Basel) 2025; 10:241. [PMID: 40277640 PMCID: PMC12024668 DOI: 10.3390/biomimetics10040241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2025] [Revised: 04/08/2025] [Accepted: 04/08/2025] [Indexed: 04/26/2025] Open
Abstract
Consciousness is liable to not be defined in scientific research, because it is an object of study in philosophy too, which actually hinders the integration of research on a large scale. The present study attempts to define consciousness with mathematical approaches by including the common meaning of consciousness across multiple disciplines. By extracting the essential characteristics of consciousness-transitivity-a categorical model of consciousness is established. This model is used to obtain three layers of categories, namely objects, materials as reflex units, and consciousness per se in homomorphism. The model forms a framework that functional neurons or AI (biomimetic) parts can be treated as variables, functions or local solutions of the model. Consequently, consciousness is quantified algebraically, which helps determining and evaluating consciousness with views that integrate nature and artifacts. Current consciousness theories and computation theories are analyzed to support the model.
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Affiliation(s)
- Yinsheng Zhang
- Institute of Scientific and Technical Information of China, Beijing 100038, China
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2
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Tononi G, Albantakis L, Barbosa L, Boly M, Cirelli C, Comolatti R, Ellia F, Findlay G, Casali AG, Grasso M, Haun AM, Hendren J, Hoel E, Koch C, Maier A, Marshall W, Massimini M, Mayner WG, Oizumi M, Szczotka J, Tsuchiya N, Zaeemzadeh A. Consciousness or pseudo-consciousness? A clash of two paradigms. Nat Neurosci 2025; 28:694-702. [PMID: 40065188 DOI: 10.1038/s41593-025-01880-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/09/2025]
Affiliation(s)
- Giulio Tononi
- Department of Psychiatry, University of Wisconsin-Madison, Madison, WI, USA.
| | - Larissa Albantakis
- Department of Psychiatry, University of Wisconsin-Madison, Madison, WI, USA
| | - Leonardo Barbosa
- Fralin Biomedical Research Institute at Virginia Tech Carilion, Roanoke, VA, USA
| | - Melanie Boly
- Department of Neurology, University of Wisconsin-Madison, Madison, WI, USA
| | - Chiara Cirelli
- Department of Psychiatry, University of Wisconsin-Madison, Madison, WI, USA
| | - Renzo Comolatti
- Department of Psychiatry, University of Wisconsin-Madison, Madison, WI, USA
- Department of Biomedical and Clinical Sciences, University of Milan, Milan, Italy
| | - Francesco Ellia
- Laboratory of Qualia Structure, ATR Computational Neuroscience Laboratories, Kyoto, Japan
- School of Psychological Sciences, Faculty of Medicine, Nursing, and Health Sciences, Monash University, Melbourne, Victoria, Australia
| | - Graham Findlay
- Department of Psychiatry, University of Wisconsin-Madison, Madison, WI, USA
| | - Adenauer Girardi Casali
- Institute of Science and Technology, Federal University of São Paulo, São José dos Campos, Brazil
| | - Matteo Grasso
- Department of Psychiatry, University of Wisconsin-Madison, Madison, WI, USA
| | - Andrew M Haun
- Department of Psychiatry, University of Wisconsin-Madison, Madison, WI, USA
| | - Jeremiah Hendren
- Graduate School Language & Literature, Ludwig Maximilian University of Munich, Munich, Germany
| | - Erik Hoel
- Allen Discovery Center, Tufts University, Boston, MA, USA
| | - Christof Koch
- Allen Institute, Seattle, WA, USA
- Tiny Blue Dot Foundation, Santa Monica, CA, USA
| | - Alexander Maier
- Department of Psychology, College of Arts and Science, Vanderbilt University, Nashville, TN, USA
| | - William Marshall
- Department of Mathematics and Statistics, Brock University, St. Catharines, Ontario, Canada
| | - Marcello Massimini
- Department of Biomedical and Clinical Sciences, University of Milan, Milan, Italy
- IRCCS Fondazione Don Carlo Gnocchi ONLUS, Milan, Italy
| | - William Gp Mayner
- Department of Psychiatry, University of Wisconsin-Madison, Madison, WI, USA
| | - Masafumi Oizumi
- Department of General Systems Studies, Graduate School of Arts and Sciences, The University of Tokyo, Tokyo, Japan
| | - Joanna Szczotka
- Department of Psychiatry, University of Wisconsin-Madison, Madison, WI, USA
| | - Naotsugu Tsuchiya
- Laboratory of Qualia Structure, ATR Computational Neuroscience Laboratories, Kyoto, Japan
- School of Psychological Sciences, Faculty of Medicine, Nursing, and Health Sciences, Monash University, Melbourne, Victoria, Australia
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3
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Kleiner J, Ludwig T. The case for neurons: a no-go theorem for consciousness on a chip. Neurosci Conscious 2024; 2024:niae037. [PMID: 39735388 PMCID: PMC11671748 DOI: 10.1093/nc/niae037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 10/27/2024] [Accepted: 10/31/2024] [Indexed: 12/31/2024] Open
Abstract
We apply the methodology of no-go theorems as developed in physics to the question of artificial consciousness. The result is a no-go theorem which shows that under a general assumption, called dynamical relevance, Artificial Intelligence (AI) systems that run on contemporary computer chips cannot be conscious. Consciousness is dynamically relevant, simply put, if, according to a theory of consciousness, it is relevant for the temporal evolution of a system's states. The no-go theorem rests on facts about semiconductor development: that AI systems run on central processing units, graphics processing units, tensor processing units, or other processors which have been designed and verified to adhere to computational dynamics that systematically preclude or suppress deviations. Whether our result resolves the question of AI consciousness on contemporary processors depends on the truth of the theorem's main assumption, dynamical relevance, which this paper does not establish.
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Affiliation(s)
- Johannes Kleiner
- Munich Center for Mathematical Philosophy, Ludwig Maximilian University of Munich, Geschwister-Scholl-Platz 1, Munich 80539, Germany
- Munich Graduate School of Systemic Neurosciences, Ludwig Maximilian University of Munich, Großhaderner Str. 2, Planegg-Martinsried 82152, Germany
- Institute for Psychology, University of Bamberg, Markusplatz 3, Bamberg 96047, Germany
- Association for Mathematical Consciousness Science, c/o M3/01.13 Markusplatz 3, Bamberg 96047, Germany
| | - Tim Ludwig
- Department of Philosophy, Institute of Technology Futures, Karlsruhe Institute of Technology, Douglasstraße 24, Karlsruhe 76133, Germany
- Institute for Theoretical Physics, Utrecht University, Princetonplein 5, Utrecht 3584 CC, The Netherlands
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4
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Martin CH, Mani G. The recent Physics and Chemistry Nobel Prizes, AI, and the convergence of knowledge fields. PATTERNS (NEW YORK, N.Y.) 2024; 5:101099. [PMID: 39776852 PMCID: PMC11701849 DOI: 10.1016/j.patter.2024.101099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/11/2025]
Abstract
This article examines the convergence of physics, chemistry, and artificial intelligence (AI), highlighted by recent Nobel Prizes. It traces the historical development of neural networks, emphasizing interdisciplinary research's role in advancing AI. The authors advocate for nurturing AI-enabled polymaths to bridge the gap between theoretical advancements and practical applications, driving progress toward artificial general intelligence (AGI).
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Affiliation(s)
| | - Ganesh Mani
- Carnegie Mellon University, Pittsburgh, PA, USA
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5
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Kirkeby-Hinrup A, Stenseke J, Overgaard MS. Evaluating the explanatory power of the Conscious Turing Machine. Conscious Cogn 2024; 124:103736. [PMID: 39163807 DOI: 10.1016/j.concog.2024.103736] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2024] [Revised: 08/06/2024] [Accepted: 08/07/2024] [Indexed: 08/22/2024]
Abstract
The recent "Conscious Turing Machine" (CTM) proposal offered by Manuel and Lenore Blum aims to define and explore consciousness, contribute to the solution of the hard problem, and demonstrate the value of theoretical computer science with respect to the study of consciousness. Surprisingly, given the ambitiousness and novelty of the proposal (and the prominence of its creators), CTM has received relatively little attention. We here seek to remedy this by offering an exhaustive evaluation of CTM. Our evaluation considers the explanatory power of CTM in three different domains of interdisciplinary consciousness studies: the philosophy of mind, cognitive neuroscience, and computation. Based on our evaluation in each of the target domains, at present, any claim that CTM constitutes progress is premature. Nevertheless, the model has potential, and we highlight several possible avenues of future research which proponents of the model may pursue in its development.
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Affiliation(s)
- Asger Kirkeby-Hinrup
- Department of Philosophy, Lund University, Sweden; Center for Functionally Integrative Neuroscience, Aarhus University, Denmark.
| | | | - Morten S Overgaard
- Center for Functionally Integrative Neuroscience, Aarhus University, Denmark
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Huang H. Eight challenges in developing theory of intelligence. Front Comput Neurosci 2024; 18:1388166. [PMID: 39114083 PMCID: PMC11303322 DOI: 10.3389/fncom.2024.1388166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2024] [Accepted: 07/09/2024] [Indexed: 08/10/2024] Open
Abstract
A good theory of mathematical beauty is more practical than any current observation, as new predictions about physical reality can be self-consistently verified. This belief applies to the current status of understanding deep neural networks including large language models and even the biological intelligence. Toy models provide a metaphor of physical reality, allowing mathematically formulating the reality (i.e., the so-called theory), which can be updated as more conjectures are justified or refuted. One does not need to present all details in a model, but rather, more abstract models are constructed, as complex systems such as the brains or deep networks have many sloppy dimensions but much less stiff dimensions that strongly impact macroscopic observables. This type of bottom-up mechanistic modeling is still promising in the modern era of understanding the natural or artificial intelligence. Here, we shed light on eight challenges in developing theory of intelligence following this theoretical paradigm. Theses challenges are representation learning, generalization, adversarial robustness, continual learning, causal learning, internal model of the brain, next-token prediction, and the mechanics of subjective experience.
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Affiliation(s)
- Haiping Huang
- PMI Lab, School of Physics, Sun Yat-sen University, Guangzhou, China
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Dossa RFJ, Arulkumaran K, Juliani A, Sasai S, Kanai R. Design and evaluation of a global workspace agent embodied in a realistic multimodal environment. Front Comput Neurosci 2024; 18:1352685. [PMID: 38948336 PMCID: PMC11211627 DOI: 10.3389/fncom.2024.1352685] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Accepted: 05/20/2024] [Indexed: 07/02/2024] Open
Abstract
As the apparent intelligence of artificial neural networks (ANNs) advances, they are increasingly likened to the functional networks and information processing capabilities of the human brain. Such comparisons have typically focused on particular modalities, such as vision or language. The next frontier is to use the latest advances in ANNs to design and investigate scalable models of higher-level cognitive processes, such as conscious information access, which have historically lacked concrete and specific hypotheses for scientific evaluation. In this work, we propose and then empirically assess an embodied agent with a structure based on global workspace theory (GWT) as specified in the recently proposed "indicator properties" of consciousness. In contrast to prior works on GWT which utilized single modalities, our agent is trained to navigate 3D environments based on realistic audiovisual inputs. We find that the global workspace architecture performs better and more robustly at smaller working memory sizes, as compared to a standard recurrent architecture. Beyond performance, we perform a series of analyses on the learned representations of our architecture and share findings that point to task complexity and regularization being essential for feature learning and the development of meaningful attentional patterns within the workspace.
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Kanai R, Fujisawa I. Toward a universal theory of consciousness. Neurosci Conscious 2024; 2024:niae022. [PMID: 38826771 PMCID: PMC11141593 DOI: 10.1093/nc/niae022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 05/08/2024] [Accepted: 05/15/2024] [Indexed: 06/04/2024] Open
Abstract
While falsifiability has been broadly discussed as a desirable property of a theory of consciousness, in this paper, we introduce the meta-theoretic concept of "Universality" as an additional desirable property for a theory of consciousness. The concept of universality, often assumed in physics, posits that the fundamental laws of nature are consistent and apply equally everywhere in the universe and remain constant over time. This assumption is crucial in science, acting as a guiding principle for developing and testing theories. When applied to theories of consciousness, universality can be defined as the ability of a theory to determine whether any fully described dynamical system is conscious or non-conscious. Importantly, for a theory to be universal, the determinant of consciousness needs to be defined as an intrinsic property of a system as opposed to replying on the interpretation of the external observer. The importance of universality originates from the consideration that given that consciousness is a natural phenomenon, it could in principle manifest in any physical system that satisfies a certain set of conditions whether it is biological or non-biological. To date, apart from a few exceptions, most existing theories do not possess this property. Instead, they tend to make predictions as to the neural correlates of consciousness based on the interpretations of brain functions, which makes those theories only applicable to brain-centric systems. While current functionalist theories of consciousness tend to be heavily reliant on our interpretations of brain functions, we argue that functionalist theories could be converted to a universal theory by specifying mathematical formulations of the constituent concepts. While neurobiological and functionalist theories retain their utility in practice, we will eventually need a universal theory to fully explain why certain types of systems possess consciousness.
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Affiliation(s)
- Ryota Kanai
- President Office, Araya, Inc., Sanpo Sakuma Building, 1-11 Kanda Sakuma-cho, Chiyoda-ku, Tokyo 101-0025, Japan
| | - Ippei Fujisawa
- President Office, Araya, Inc., Sanpo Sakuma Building, 1-11 Kanda Sakuma-cho, Chiyoda-ku, Tokyo 101-0025, Japan
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9
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Kleiner J. Towards a structural turn in consciousness science. Conscious Cogn 2024; 119:103653. [PMID: 38422757 DOI: 10.1016/j.concog.2024.103653] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Revised: 01/22/2024] [Accepted: 01/30/2024] [Indexed: 03/02/2024]
Abstract
Recent activities in virtually all fields engaged in consciousness studies indicate early signs of a structural turn, where verbal descriptions or simple formalisations of conscious experiences are replaced by structural tools, most notably mathematical spaces. My goal here is to offer three comments that, in my opinion, are essential to avoid misunderstandings in these developments early on. These comments concern metaphysical premises of structural approaches, the viability of structure-preserving mappings, and the question of what a structure of conscious experience is in the first place. I will also explain what, in my opinion, are the great promises of structural methodologies and how they might impact consciousness science at large.
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Affiliation(s)
- Johannes Kleiner
- Munich Center for Mathematical Philosophy, Ludwig-Maximilians-Universität München, Geschwister-Scholl-Platz 1, 80539 München, Germany; Graduate School of Systemic Neurosciences, Ludwig-Maximilians-Universität München, Großhaderner Str. 2, 82152 Planegg-Martinsried, Germany; Institute for Psychology, University of Bamberg, Markusplatz 3, 96047 Bamberg, Germany; Association for Mathematical Consciousness Science, Geschwister-Scholl-Platz 1, 80539 München, Germany.
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10
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Volzhenin K, Changeux JP, Dumas G. Multilevel development of cognitive abilities in an artificial neural network. Proc Natl Acad Sci U S A 2022; 119:e2201304119. [PMID: 36122214 PMCID: PMC9522351 DOI: 10.1073/pnas.2201304119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Accepted: 08/16/2022] [Indexed: 11/18/2022] Open
Abstract
Several neuronal mechanisms have been proposed to account for the formation of cognitive abilities through postnatal interactions with the physical and sociocultural environment. Here, we introduce a three-level computational model of information processing and acquisition of cognitive abilities. We propose minimal architectural requirements to build these levels, and how the parameters affect their performance and relationships. The first sensorimotor level handles local nonconscious processing, here during a visual classification task. The second level or cognitive level globally integrates the information from multiple local processors via long-ranged connections and synthesizes it in a global, but still nonconscious, manner. The third and cognitively highest level handles the information globally and consciously. It is based on the global neuronal workspace (GNW) theory and is referred to as the conscious level. We use the trace and delay conditioning tasks to, respectively, challenge the second and third levels. Results first highlight the necessity of epigenesis through the selection and stabilization of synapses at both local and global scales to allow the network to solve the first two tasks. At the global scale, dopamine appears necessary to properly provide credit assignment despite the temporal delay between perception and reward. At the third level, the presence of interneurons becomes necessary to maintain a self-sustained representation within the GNW in the absence of sensory input. Finally, while balanced spontaneous intrinsic activity facilitates epigenesis at both local and global scales, the balanced excitatory/inhibitory ratio increases performance. We discuss the plausibility of the model in both neurodevelopmental and artificial intelligence terms.
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Affiliation(s)
- Konstantin Volzhenin
- Neuroscience Department, Institut Pasteur, 75015 Paris, France
- Laboratory of Computational and Quantitative Biology, Sorbonne Université, 75005 Paris, France
| | | | - Guillaume Dumas
- Neuroscience Department, Institut Pasteur, 75015 Paris, France
- Mila - Quebec Artificial Intelligence Institute, Centre Hospitalier Universitaire Sainte-Justine Research Center, Department of Psychiatry, Université de Montréal, Montréal, QC H3T 1C5, Canada
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12
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Blum L, Blum M. A theory of consciousness from a theoretical computer science perspective: Insights from the Conscious Turing Machine. Proc Natl Acad Sci U S A 2022; 119:e2115934119. [PMID: 35594400 PMCID: PMC9171770 DOI: 10.1073/pnas.2115934119] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Accepted: 03/22/2022] [Indexed: 11/25/2022] Open
Abstract
This paper examines consciousness from the perspective of theoretical computer science (TCS), a branch of mathematics concerned with understanding the underlying principles of computation and complexity, including the implications and surprising consequences of resource limitations. We propose a formal TCS model, the Conscious Turing Machine (CTM). The CTM is influenced by Alan Turing's simple yet powerful model of computation, the Turing machine (TM), and by the global workspace theory (GWT) of consciousness originated by cognitive neuroscientist Bernard Baars and further developed by him, Stanislas Dehaene, Jean-Pierre Changeux, George Mashour, and others. Phenomena generally associated with consciousness, such as blindsight, inattentional blindness, change blindness, dream creation, and free will, are considered. Explanations derived from the model draw confirmation from consistencies at a high level, well above the level of neurons, with the cognitive neuroscience literature.
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Affiliation(s)
- Lenore Blum
- Computer Science Department, Carnegie Mellon University, Pittsburgh, PA 15213
- Electrical Engineering and Computer Science (EECS), University of California, Berkeley, CA 94720
| | - Manuel Blum
- Computer Science Department, Carnegie Mellon University, Pittsburgh, PA 15213
- Electrical Engineering and Computer Science (EECS), University of California, Berkeley, CA 94720
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