1
|
Ciaunica A, Levin M, Rosas FE, Friston K. Nested Selves: Self-Organization and Shared Markov Blankets in Prenatal Development in Humans. Top Cogn Sci 2023. [PMID: 38158882 DOI: 10.1111/tops.12717] [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: 05/17/2023] [Revised: 12/05/2023] [Accepted: 12/06/2023] [Indexed: 01/03/2024]
Abstract
The immune system is a central component of organismic function in humans. This paper addresses self-organization of biological systems in relation to-and nested within-other biological systems in pregnancy. Pregnancy constitutes a fundamental state for human embodiment and a key step in the evolution and conservation of our species. While not all humans can be pregnant, our initial state of emerging and growing within another person's body is universal. Hence, the pregnant state does not concern some individuals but all individuals. Indeed, the hierarchical relationship in pregnancy reflects an even earlier autopoietic process in the embryo by which the number of individuals in a single blastoderm is dynamically determined by cell- interactions. The relationship and the interactions between the two self-organizing systems during pregnancy may play a pivotal role in understanding the nature of biological self-organization per se in humans. Specifically, we consider the role of the immune system in biological self-organization in addition to neural/brain systems that furnish us with a sense of self. We examine the complex case of pregnancy, whereby two immune systems need to negotiate the exchange of resources and information in order to maintain viable self-regulation of nested systems. We conclude with a proposal for the mechanisms-that scaffold the complex relationship between two self-organising systems in pregnancy-through the lens of the Active Inference, with a focus on shared Markov blankets.
Collapse
Affiliation(s)
- Anna Ciaunica
- Centre for Philosophy of Science (CFCUL), University of Lisbon
- Institute of Cognitive Neuroscience, University College London
| | - Michael Levin
- Department of Biology and Allen Discovery Center, Tufts University
| | - Fernando E Rosas
- Department of Informatics, University of Sussex
- Centre for Complexity Science, Imperial College London
- Department of Brain Sciences, Imperial College London
- Centre for Eudaimonia and Human Flourishing, University of Oxford
| | - Karl Friston
- Welcome Centre for Human Neuroimaging, University College London
- VERSES AI Research Lab
| |
Collapse
|
2
|
Ramstead MJD, Sakthivadivel DAR, Heins C, Koudahl M, Millidge B, Da Costa L, Klein B, Friston KJ. On Bayesian mechanics: a physics of and by beliefs. Interface Focus 2023; 13:20220029. [PMID: 37213925 PMCID: PMC10198254 DOI: 10.1098/rsfs.2022.0029] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Accepted: 01/17/2023] [Indexed: 05/23/2023] Open
Abstract
The aim of this paper is to introduce a field of study that has emerged over the last decade, called Bayesian mechanics. Bayesian mechanics is a probabilistic mechanics, comprising tools that enable us to model systems endowed with a particular partition (i.e. into particles), where the internal states (or the trajectories of internal states) of a particular system encode the parameters of beliefs about external states (or their trajectories). These tools allow us to write down mechanical theories for systems that look as if they are estimating posterior probability distributions over the causes of their sensory states. This provides a formal language for modelling the constraints, forces, potentials and other quantities determining the dynamics of such systems, especially as they entail dynamics on a space of beliefs (i.e. on a statistical manifold). Here, we will review the state of the art in the literature on the free energy principle, distinguishing between three ways in which Bayesian mechanics has been applied to particular systems (i.e. path-tracking, mode-tracking and mode-matching). We go on to examine a duality between the free energy principle and the constrained maximum entropy principle, both of which lie at the heart of Bayesian mechanics, and discuss its implications.
Collapse
Affiliation(s)
- Maxwell J. D. Ramstead
- VERSES Research Lab, Los Angeles, CA 90016, USA
- Wellcome Centre for Human Neuroimaging, University College London, London WC1N 3AR, UK
| | - Dalton A. R. Sakthivadivel
- VERSES Research Lab, Los Angeles, CA 90016, USA
- Department of Mathematics, Stony Brook University, Stony Brook, NY, USA
- Department of Physics and Astronomy, Stony Brook University, Stony Brook, NY, USA
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, USA
| | - Conor Heins
- VERSES Research Lab, Los Angeles, CA 90016, USA
- Department of Collective Behaviour, Max Planck Institute of Animal Behavior, 78464 Konstanz, Germany
- Department of Biology, University of Konstanz, 78464 Konstanz, Germany
- Centre for the Advanced Study of Collective Behaviour, University of Konstanz, 78464 Konstanz, Germany
| | - Magnus Koudahl
- VERSES Research Lab, Los Angeles, CA 90016, USA
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Beren Millidge
- VERSES Research Lab, Los Angeles, CA 90016, USA
- Brain Network Dynamics Unit, University of Oxford, Oxford, UK
| | - Lancelot Da Costa
- Wellcome Centre for Human Neuroimaging, University College London, London WC1N 3AR, UK
- Department of Mathematics, Imperial College London, London SW7 2AZ, UK
| | - Brennan Klein
- VERSES Research Lab, Los Angeles, CA 90016, USA
- Network Science Institute, Northeastern University, Boston, MA, USA
| | - Karl J. Friston
- VERSES Research Lab, Los Angeles, CA 90016, USA
- Wellcome Centre for Human Neuroimaging, University College London, London WC1N 3AR, UK
| |
Collapse
|
3
|
Aguilera M, Millidge B, Tschantz A, Buckley CL. From the free energy principle to a confederation of Bayesian mechanics: Reply to comments on "How particular is the physics of the free energy principle?". Phys Life Rev 2023; 44:270-275. [PMID: 36821891 DOI: 10.1016/j.plrev.2023.01.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Accepted: 01/30/2023] [Indexed: 02/10/2023]
Affiliation(s)
- Miguel Aguilera
- BCAM - Basque Center for Applied Mathematics, Bilbao, 480009, Spain; IKERBASQUE, Basque Foundation for Science, Bilbao, 480009, Spain; School of Engineering and Informatics, University of Sussex, Falmer, Brighton, BN1 9QJ, United Kingdom.
| | - Beren Millidge
- VERSES Research Lab, Los Angeles, 2JC7+WX, CA, USA; MRC Brain Network Dynamics Unit, University of Oxford, Oxford, OX1 3TH, United Kingdom
| | | | - Christopher L Buckley
- School of Engineering and Informatics, University of Sussex, Falmer, Brighton, BN1 9QJ, United Kingdom; VERSES Research Lab, Los Angeles, 2JC7+WX, CA, USA
| |
Collapse
|
4
|
Thermodynamic fluctuation theorems govern human sensorimotor learning. Sci Rep 2023; 13:869. [PMID: 36650215 PMCID: PMC9845310 DOI: 10.1038/s41598-023-27736-8] [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: 09/07/2022] [Accepted: 01/06/2023] [Indexed: 01/18/2023] Open
Abstract
The application of thermodynamic reasoning in the study of learning systems has a long tradition. Recently, new tools relating perfect thermodynamic adaptation to the adaptation process have been developed. These results, known as fluctuation theorems, have been tested experimentally in several physical scenarios and, moreover, they have been shown to be valid under broad mathematical conditions. Hence, although not experimentally challenged yet, they are presumed to apply to learning systems as well. Here we address this challenge by testing the applicability of fluctuation theorems in learning systems, more specifically, in human sensorimotor learning. In particular, we relate adaptive movement trajectories in a changing visuomotor rotation task to fully adapted steady-state behavior of individual participants. We find that human adaptive behavior in our task is generally consistent with fluctuation theorem predictions and discuss the merits and limitations of the approach.
Collapse
|
5
|
Hack P, Gottwald S, Braun DA. Jarzyski's Equality and Crooks' Fluctuation Theorem for General Markov Chains with Application to Decision-Making Systems. ENTROPY (BASEL, SWITZERLAND) 2022; 24:1731. [PMID: 36554136 PMCID: PMC9777588 DOI: 10.3390/e24121731] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Revised: 11/19/2022] [Accepted: 11/24/2022] [Indexed: 06/17/2023]
Abstract
We define common thermodynamic concepts purely within the framework of general Markov chains and derive Jarzynski's equality and Crooks' fluctuation theorem in this setup. In particular, we regard the discrete-time case, which leads to an asymmetry in the definition of work that appears in the usual formulation of Crooks' fluctuation theorem. We show how this asymmetry can be avoided with an additional condition regarding the energy protocol. The general formulation in terms of Markov chains allows transferring the results to other application areas outside of physics. Here, we discuss how this framework can be applied in the context of decision-making. This involves the definition of the relevant quantities, the assumptions that need to be made for the different fluctuation theorems to hold, as well as the consideration of discrete trajectories instead of the continuous trajectories, which are relevant in physics.
Collapse
|
6
|
Enough blanket metaphysics, time for data-driven heuristics. Behav Brain Sci 2022; 45:e206. [PMID: 36172760 DOI: 10.1017/s0140525x22000280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Bruineberg and colleagues criticisms' have been received but downplayed in the free energy principle (FEP) literature. We strengthen their points, arguing that Friston blanket discovery, even if tractable, requires a full formal description of the system of interest at the outset. Hence, blanket metaphysics is futile, and we postulate that researchers should turn back to heuristic uses of Pearl blankets.
Collapse
|
7
|
The Emperor Is Naked: Replies to commentaries on the target article. Behav Brain Sci 2022; 45:e219. [PMID: 36172792 DOI: 10.1017/s0140525x22000656] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
The 35 commentaries cover a wide range of topics and take many different stances on the issues explored by the target article. We have organised our response to the commentaries around three central questions: Are Friston blankets just Pearl blankets? What ontological and metaphysical commitments are implied by the use of Friston blankets? What kind of explanatory work are Friston blankets capable of? We conclude our reply with a short critical reflection on the indiscriminate use of both Markov blankets and the free energy principle.
Collapse
|
8
|
Heins C. Particular flows and attracting sets: A comment on "How particular is the physics of the free energy principle?" by Aguilera, Millidge, Tschantz and Buckley. Phys Life Rev 2022; 42:43-48. [PMID: 35738072 DOI: 10.1016/j.plrev.2022.06.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2022] [Accepted: 06/09/2022] [Indexed: 11/18/2022]
Affiliation(s)
- Conor Heins
- Department of Collective Behaviour, Max Planck Institute of Animal Behavior, 78464 Konstanz, Germany; Department of Biology, University of Konstanz, 78464 Konstanz, Germany; Centre for the Advanced Study of Collective Behaviour, University of Konstanz, 78464 Konstanz, Germany; VERSES Research Labs, Los Angeles, CA, USA.
| |
Collapse
|
9
|
Heins C, Da Costa L. Sparse coupling and Markov blankets: A comment on "How particular is the physics of the Free Energy Principle?" by Aguilera, Millidge, Tschantz and Buckley. Phys Life Rev 2022; 42:33-39. [PMID: 35724536 DOI: 10.1016/j.plrev.2022.06.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Accepted: 06/09/2022] [Indexed: 11/20/2022]
Affiliation(s)
- Conor Heins
- Department of Collective Behaviour, Max Planck Institute of Animal Behavior, 78464 Konstanz, Germany; Department of Biology, University of Konstanz, 78464 Konstanz, Germany; Centre for the Advanced Study of Collective Behaviour, University of Konstanz, 78464 Konstanz, Germany; VERSES Research Labs, Los Angeles, CA, USA.
| | - Lancelot Da Costa
- Department of Mathematics, Imperial College London, London SW7 2AZ, UK; Wellcome Centre for Human Neuroimaging, University College London, London WC1N 3AR, UK
| |
Collapse
|
10
|
Some minimal notes on notation and minima. Phys Life Rev 2022; 42:4-7. [DOI: 10.1016/j.plrev.2022.05.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Accepted: 05/11/2022] [Indexed: 11/21/2022]
|
11
|
Hipólito I, van Es T. Enactive-Dynamic Social Cognition and Active Inference. Front Psychol 2022; 13:855074. [PMID: 35572328 PMCID: PMC9102990 DOI: 10.3389/fpsyg.2022.855074] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Accepted: 03/16/2022] [Indexed: 11/17/2022] Open
Abstract
This aim of this paper is two-fold: it critically analyses and rejects accounts blending active inference as theory of mind and enactivism; and it advances an enactivist-dynamic understanding of social cognition that is compatible with active inference. While some social cognition theories seemingly take an enactive perspective on social cognition, they explain it as the attribution of mental states to other people, by assuming representational structures, in line with the classic Theory of Mind (ToM). Holding both enactivism and ToM, we argue, entails contradiction and confusion due to two ToM assumptions widely known to be rejected by enactivism: that (1) social cognition reduces to mental representation and (2) social cognition is a hardwired contentful ‘toolkit’ or ‘starter pack’ that fuels the model-like theorising supposed in (1). The paper offers a positive alternative, one that avoids contradictions or confusion. After rejecting ToM-inspired theories of social cognition and clarifying the profile of social cognition under enactivism, that is without assumptions (1) and (2), the last section advances an enactivist-dynamic model of cognition as dynamic, real-time, fluid, contextual social action, where we use the formalisms of dynamical systems theory to explain the origins of socio-cognitive novelty in developmental change and active inference as a tool to demonstrate social understanding as generalised synchronisation.
Collapse
Affiliation(s)
- Inês Hipólito
- Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany
- Department of Psychology, Amsterdam Brain and Cognition, University of Amsterdam, Amsterdam, Netherlands
- *Correspondence: Inês Hipólito,
| | - Thomas van Es
- Centre for Philosophical Psychology, Universiteit Antwerpen, Antwerp, Belgium
| |
Collapse
|
12
|
Albarracin M, Demekas D, Ramstead MJD, Heins C. Epistemic Communities under Active Inference. ENTROPY 2022; 24:e24040476. [PMID: 35455140 PMCID: PMC9027706 DOI: 10.3390/e24040476] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Revised: 03/11/2022] [Accepted: 03/24/2022] [Indexed: 02/04/2023]
Abstract
The spread of ideas is a fundamental concern of today’s news ecology. Understanding the dynamics of the spread of information and its co-option by interested parties is of critical importance. Research on this topic has shown that individuals tend to cluster in echo-chambers and are driven by confirmation bias. In this paper, we leverage the active inference framework to provide an in silico model of confirmation bias and its effect on echo-chamber formation. We build a model based on active inference, where agents tend to sample information in order to justify their own view of reality, which eventually leads to them to have a high degree of certainty about their own beliefs. We show that, once agents have reached a certain level of certainty about their beliefs, it becomes very difficult to get them to change their views. This system of self-confirming beliefs is upheld and reinforced by the evolving relationship between an agent’s beliefs and observations, which over time will continue to provide evidence for their ingrained ideas about the world. The epistemic communities that are consolidated by these shared beliefs, in turn, tend to produce perceptions of reality that reinforce those shared beliefs. We provide an active inference account of this community formation mechanism. We postulate that agents are driven by the epistemic value that they obtain from sampling or observing the behaviours of other agents. Inspired by digital social networks like Twitter, we build a generative model in which agents generate observable social claims or posts (e.g., ‘tweets’) while reading the socially observable claims of other agents that lend support to one of two mutually exclusive abstract topics. Agents can choose which other agent they pay attention to at each timestep, and crucially who they attend to and what they choose to read influences their beliefs about the world. Agents also assess their local network’s perspective, influencing which kinds of posts they expect to see other agents making. The model was built and simulated using the freely available Python package pymdp. The proposed active inference model can reproduce the formation of echo-chambers over social networks, and gives us insight into the cognitive processes that lead to this phenomenon.
Collapse
Affiliation(s)
- Mahault Albarracin
- Department of Cognitive Computing, Université du Québec a Montreal, Montreal, QC H2K 4M1, Canada;
- VERSES Labs, Los Angeles, CA 90016, USA;
| | - Daphne Demekas
- Department of Computing, Imperial College London, London SW7 5NH, UK;
| | - Maxwell J. D. Ramstead
- VERSES Labs, Los Angeles, CA 90016, USA;
- Wellcome Centre for Human Neuroimaging, University College London, London WC1N 3AR, UK
| | - Conor Heins
- VERSES Labs, Los Angeles, CA 90016, USA;
- Department of Collective Behaviour, Max Planck Institute of Animal Behaviour, 78315 Radolfzell am Bodensee, Germany
- Centre for the Advanced Study of Collective Behaviour, University of Konstanz, 78464 Konstanz, Germany
- Department of Biology, University of Konstanz, 78457 Konstanz, Germany
- Correspondence:
| |
Collapse
|