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Fields C, Levin M. Regulative development as a model for origin of life and artificial life studies. Biosystems 2023; 229:104927. [PMID: 37211257 DOI: 10.1016/j.biosystems.2023.104927] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 05/03/2023] [Accepted: 05/08/2023] [Indexed: 05/23/2023]
Abstract
Using the formal framework of the Free Energy Principle, we show how generic thermodynamic requirements on bidirectional information exchange between a system and its environment can generate complexity. This leads to the emergence of hierarchical computational architectures in systems that operate sufficiently far from thermal equilibrium. In this setting, the environment of any system increases its ability to predict system behavior by "engineering" the system towards increased morphological complexity and hence larger-scale, more macroscopic behaviors. When seen in this light, regulative development becomes an environmentally-driven process in which "parts" are assembled to produce a system with predictable behavior. We suggest on this basis that life is thermodynamically favorable and that, when designing artificial living systems, human engineers are acting like a generic "environment".
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Affiliation(s)
- Chris Fields
- Allen Discovery Center at Tufts University, Medford, MA, 02155, USA.
| | - Michael Levin
- Allen Discovery Center at Tufts University, Medford, MA, 02155, USA; Wyss Institute for Biologically Inspired Engineering at Harvard University, Boston, MA, 02115, USA
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Fields C, Levin M. Competency in Navigating Arbitrary Spaces as an Invariant for Analyzing Cognition in Diverse Embodiments. ENTROPY 2022; 24:e24060819. [PMID: 35741540 PMCID: PMC9222757 DOI: 10.3390/e24060819] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Revised: 05/26/2022] [Accepted: 06/08/2022] [Indexed: 12/20/2022]
Abstract
One of the most salient features of life is its capacity to handle novelty and namely to thrive and adapt to new circumstances and changes in both the environment and internal components. An understanding of this capacity is central to several fields: the evolution of form and function, the design of effective strategies for biomedicine, and the creation of novel life forms via chimeric and bioengineering technologies. Here, we review instructive examples of living organisms solving diverse problems and propose competent navigation in arbitrary spaces as an invariant for thinking about the scaling of cognition during evolution. We argue that our innate capacity to recognize agency and intelligence in unfamiliar guises lags far behind our ability to detect it in familiar behavioral contexts. The multi-scale competency of life is essential to adaptive function, potentiating evolution and providing strategies for top-down control (not micromanagement) to address complex disease and injury. We propose an observer-focused viewpoint that is agnostic about scale and implementation, illustrating how evolution pivoted similar strategies to explore and exploit metabolic, transcriptional, morphological, and finally 3D motion spaces. By generalizing the concept of behavior, we gain novel perspectives on evolution, strategies for system-level biomedical interventions, and the construction of bioengineered intelligences. This framework is a first step toward relating to intelligence in highly unfamiliar embodiments, which will be essential for progress in artificial intelligence and regenerative medicine and for thriving in a world increasingly populated by synthetic, bio-robotic, and hybrid beings.
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Affiliation(s)
- Chris Fields
- Allen Discovery Center at Tufts University, Science and Engineering Complex, 200 College Ave., Medford, MA 02155, USA;
| | - Michael Levin
- Allen Discovery Center at Tufts University, Science and Engineering Complex, 200 College Ave., Medford, MA 02155, USA;
- Wyss Institute for Biologically Inspired Engineering at Harvard University, Boston, MA 02115, USA
- Correspondence:
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Fields C, Glazebrook JF, Levin M. Neurons as hierarchies of quantum reference frames. Biosystems 2022; 219:104714. [PMID: 35671840 DOI: 10.1016/j.biosystems.2022.104714] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Revised: 05/28/2022] [Accepted: 05/28/2022] [Indexed: 11/19/2022]
Abstract
Conceptual and mathematical models of neurons have lagged behind empirical understanding for decades. Here we extend previous work in modeling biological systems with fully scale-independent quantum information-theoretic tools to develop a uniform, scalable representation of synapses, dendritic and axonal processes, neurons, and local networks of neurons. In this representation, hierarchies of quantum reference frames act as hierarchical active-inference systems. The resulting model enables specific predictions of correlations between synaptic activity, dendritic remodeling, and trophic reward. We summarize how the model may be generalized to nonneural cells and tissues in developmental and regenerative contexts.
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Affiliation(s)
- Chris Fields
- 23 Rue des Lavandières, 11160 Caunes Minervois, France.
| | - James F Glazebrook
- Department of Mathematics and Computer Science, Eastern Illinois University, Charleston, IL 61920, USA; Adjunct Faculty, Department of Mathematics, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Michael Levin
- Allen Discovery Center at Tufts University, Medford, MA 02155, USA
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Fields C, Friston K, Glazebrook JF, Levin M. A free energy principle for generic quantum systems. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2022; 173:36-59. [PMID: 35618044 DOI: 10.1016/j.pbiomolbio.2022.05.006] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/26/2022] [Revised: 05/04/2022] [Accepted: 05/18/2022] [Indexed: 01/17/2023]
Abstract
The Free Energy Principle (FEP) states that under suitable conditions of weak coupling, random dynamical systems with sufficient degrees of freedom will behave so as to minimize an upper bound, formalized as a variational free energy, on surprisal (a.k.a., self-information). This upper bound can be read as a Bayesian prediction error. Equivalently, its negative is a lower bound on Bayesian model evidence (a.k.a., marginal likelihood). In short, certain random dynamical systems evince a kind of self-evidencing. Here, we reformulate the FEP in the formal setting of spacetime-background free, scale-free quantum information theory. We show how generic quantum systems can be regarded as observers, which with the standard freedom of choice assumption become agents capable of assigning semantics to observational outcomes. We show how such agents minimize Bayesian prediction error in environments characterized by uncertainty, insufficient learning, and quantum contextuality. We show that in its quantum-theoretic formulation, the FEP is asymptotically equivalent to the Principle of Unitarity. Based on these results, we suggest that biological systems employ quantum coherence as a computational resource and - implicitly - as a communication resource. We summarize a number of problems for future research, particularly involving the resources required for classical communication and for detecting and responding to quantum context switches.
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Affiliation(s)
- Chris Fields
- 23 Rue des Lavandières, 11160, Caunes Minervois, France.
| | - Karl Friston
- Wellcome Centre for Human Neuroimaging, University College London, London, WC1N 3AR, UK
| | - James F Glazebrook
- Department of Mathematics and Computer Science, Eastern Illinois University, Charleston, IL, 61920, USA; Adjunct Faculty, Department of Mathematics, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
| | - Michael Levin
- Allen Discovery Center at Tufts University, Medford, MA, 02155, USA
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Kuchling F, Fields C, Levin M. Metacognition as a Consequence of Competing Evolutionary Time Scales. ENTROPY 2022; 24:e24050601. [PMID: 35626486 PMCID: PMC9141326 DOI: 10.3390/e24050601] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Revised: 04/15/2022] [Accepted: 04/19/2022] [Indexed: 12/24/2022]
Abstract
Evolution is full of coevolving systems characterized by complex spatio-temporal interactions that lead to intertwined processes of adaptation. Yet, how adaptation across multiple levels of temporal scales and biological complexity is achieved remains unclear. Here, we formalize how evolutionary multi-scale processing underlying adaptation constitutes a form of metacognition flowing from definitions of metaprocessing in machine learning. We show (1) how the evolution of metacognitive systems can be expected when fitness landscapes vary on multiple time scales, and (2) how multiple time scales emerge during coevolutionary processes of sufficiently complex interactions. After defining a metaprocessor as a regulator with local memory, we prove that metacognition is more energetically efficient than purely object-level cognition when selection operates at multiple timescales in evolution. Furthermore, we show that existing modeling approaches to coadaptation and coevolution—here active inference networks, predator–prey interactions, coupled genetic algorithms, and generative adversarial networks—lead to multiple emergent timescales underlying forms of metacognition. Lastly, we show how coarse-grained structures emerge naturally in any resource-limited system, providing sufficient evidence for metacognitive systems to be a prevalent and vital component of (co-)evolution. Therefore, multi-scale processing is a necessary requirement for many evolutionary scenarios, leading to de facto metacognitive evolutionary outcomes.
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Affiliation(s)
- Franz Kuchling
- Department of Biology, Allen Discovery Center at Tufts University, Medford, MA 02155, USA;
| | - Chris Fields
- 23 Rue des Lavandières, 11160 Caunes Minervois, France;
| | - Michael Levin
- Department of Biology, Allen Discovery Center at Tufts University, Medford, MA 02155, USA;
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02138, USA
- Correspondence:
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Fields C, Glazebrook JF, Levin M. Minimal physicalism as a scale-free substrate for cognition and consciousness. Neurosci Conscious 2021; 2021:niab013. [PMID: 34345441 PMCID: PMC8327199 DOI: 10.1093/nc/niab013] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2021] [Revised: 04/04/2021] [Accepted: 04/05/2021] [Indexed: 12/14/2022] Open
Abstract
Theories of consciousness and cognition that assume a neural substrate automatically regard phylogenetically basal, nonneural systems as nonconscious and noncognitive. Here, we advance a scale-free characterization of consciousness and cognition that regards basal systems, including synthetic constructs, as not only informative about the structure and function of experience in more complex systems but also as offering distinct advantages for experimental manipulation. Our "minimal physicalist" approach makes no assumptions beyond those of quantum information theory, and hence is applicable from the molecular scale upwards. We show that standard concepts including integrated information, state broadcasting via small-world networks, and hierarchical Bayesian inference emerge naturally in this setting, and that common phenomena including stigmergic memory, perceptual coarse-graining, and attention switching follow directly from the thermodynamic requirements of classical computation. We show that the self-representation that lies at the heart of human autonoetic awareness can be traced as far back as, and serves the same basic functions as, the stress response in bacteria and other basal systems.
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Affiliation(s)
- Chris Fields
- 23 Rue des Lavandières, 11160 Caunes Minervois, France
| | - James F Glazebrook
- Department of Mathematics and Computer Science, Eastern Illinois University, 600 Lincoln Ave, Charleston, IL 61920 USA
- Department of Mathematics, Adjunct Faculty, University of Illinois at Urbana–Champaign, 1409 W. Green Street, Urbana, IL 61801, USA
| | - Michael Levin
- Allen Discovery Center, Tufts University, 200 College Avenue, Medford, MA 02155, USA
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Dietrich E, Fields C, Hoffman DD, Prentner R. Editorial: Epistemic Feelings: Phenomenology, Implementation, and Role in Cognition. Front Psychol 2020; 11:606046. [PMID: 33192954 PMCID: PMC7652734 DOI: 10.3389/fpsyg.2020.606046] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Accepted: 09/30/2020] [Indexed: 11/13/2022] Open
Affiliation(s)
- Eric Dietrich
- Department of Philosophy, Binghamton University, Binghamton, NY, United States
| | - Chris Fields
- Independent Researcher, Caunes-Minervois, France
| | - Donald D Hoffman
- Department of Cognitive Science, University of California, Irvine, Irvine, CA, United States
| | - Robert Prentner
- Department of Cognitive Science, University of California, Irvine, Irvine, CA, United States
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Abstract
Meaning has traditionally been regarded as a problem for philosophers and psychologists. Advances in cognitive science since the early 1960s, however, broadened discussions of meaning, or more technically, the semantics of perceptions, representations, and/or actions, into biology and computer science. Here, we review the notion of “meaning” as it applies to living systems, and argue that the question of how living systems create meaning unifies the biological and cognitive sciences across both organizational and temporal scales.
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Fields C, Glazebrook JF. Information flow in context-dependent hierarchical Bayesian inference. J EXP THEOR ARTIF IN 2020. [DOI: 10.1080/0952813x.2020.1836034] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Affiliation(s)
| | - James F. Glazebrook
- Department of Mathematics and Computer Science Eastern, Illinois University, Charleston, IL, USA
- Adjunct Faculty Department of Mathematics, University of Illinois at Urbana–Champaign, Urbana, IL, USA
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