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Collective behavior from surprise minimization.

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος: Collective behavior from surprise minimization.
Συγγραφείς: Heins C; Department of Collective Behaviour, Max Planck Institute of Animal Behavior, Konstanz D-78457, Germany.; Centre for the Advanced Study of Collective Behaviour, University of Konstanz, Konstanz D-78457, Germany.; Department of Biology, University of Konstanz, Konstanz D-78457, Germany.; VERSES Research Lab, Los Angeles, CA 90016., Millidge B; Medical Research Council Brain Networks Dynamics Unit, University of Oxford, Oxford OX1 3TH, United Kingdom., Da Costa L; VERSES Research Lab, Los Angeles, CA 90016.; Department of Mathematics, Imperial College London, London SW7 2AZ, United Kingdom.; Wellcome Centre for Human Neuroimaging, University College London, London WC1N 3AR, United Kingdom., Mann RP; Department of Statistics, School of Mathematics, University of Leeds, Leeds LS2 9JT, United Kingdom., Friston KJ; VERSES Research Lab, Los Angeles, CA 90016.; Wellcome Centre for Human Neuroimaging, University College London, London WC1N 3AR, United Kingdom., Couzin ID; Department of Collective Behaviour, Max Planck Institute of Animal Behavior, Konstanz D-78457, Germany.; Centre for the Advanced Study of Collective Behaviour, University of Konstanz, Konstanz D-78457, Germany.; Department of Biology, University of Konstanz, Konstanz D-78457, Germany.
Πηγή: Proceedings of the National Academy of Sciences of the United States of America [Proc Natl Acad Sci U S A] 2024 Apr 23; Vol. 121 (17), pp. e2320239121. Date of Electronic Publication: 2024 Apr 17.
Τύπος έκδοσης: Journal Article; Research Support, N.I.H., Extramural
Γλώσσα: English
Στοιχεία περιοδικού: Publisher: National Academy of Sciences Country of Publication: United States NLM ID: 7505876 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1091-6490 (Electronic) Linking ISSN: 00278424 NLM ISO Abbreviation: Proc Natl Acad Sci U S A Subsets: MEDLINE
Imprint Name(s): Original Publication: Washington, DC : National Academy of Sciences
Ιατρικοί όροι (MeSH): Mass Behavior* , Models, Biological*, Animals ; Bayes Theorem ; Movement ; Motion ; Fishes ; Social Behavior ; Behavior, Animal
Περίληψη: Collective motion is ubiquitous in nature; groups of animals, such as fish, birds, and ungulates appear to move as a whole, exhibiting a rich behavioral repertoire that ranges from directed movement to milling to disordered swarming. Typically, such macroscopic patterns arise from decentralized, local interactions among constituent components (e.g., individual fish in a school). Preeminent models of this process describe individuals as self-propelled particles, subject to self-generated motion and "social forces" such as short-range repulsion and long-range attraction or alignment. However, organisms are not particles; they are probabilistic decision-makers. Here, we introduce an approach to modeling collective behavior based on active inference. This cognitive framework casts behavior as the consequence of a single imperative: to minimize surprise. We demonstrate that many empirically observed collective phenomena, including cohesion, milling, and directed motion, emerge naturally when considering behavior as driven by active Bayesian inference-without explicitly building behavioral rules or goals into individual agents. Furthermore, we show that active inference can recover and generalize the classical notion of social forces as agents attempt to suppress prediction errors that conflict with their expectations. By exploring the parameter space of the belief-based model, we reveal nontrivial relationships between the individual beliefs and group properties like polarization and the tendency to visit different collective states. We also explore how individual beliefs about uncertainty determine collective decision-making accuracy. Finally, we show how agents can update their generative model over time, resulting in groups that are collectively more sensitive to external fluctuations and encode information more robustly.
Competing Interests: Competing interests statement:The authors declare no competing interest.
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Brain Cogn. 2017 Mar;112:92-97. (PMID: 26809759)
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Trends Cogn Sci. 2012 Jan;16(1):72-80. (PMID: 22177032)
Proc Natl Acad Sci U S A. 2019 Oct 8;116(41):20556-20561. (PMID: 31548427)
Patterns (N Y). 2022 Nov 23;3(12):100639. (PMID: 36569556)
PLoS One. 2023 Sep 7;18(9):e0286810. (PMID: 37676869)
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Brain Struct Funct. 2013 May;218(3):611-43. (PMID: 23129312)
Phys Rev Lett. 1995 Aug 7;75(6):1226-1229. (PMID: 10060237)
Science. 2015 Jun 19;348(6241):1358-61. (PMID: 26089514)
Curr Biol. 2013 Sep 9;23(17):R709-11. (PMID: 24028946)
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Phys Rev E Stat Nonlin Soft Matter Phys. 2006 Aug;74(2 Pt 1):022101. (PMID: 17025488)
PLoS One. 2013;8(3):e58525. (PMID: 23555585)
Grant Information: United Kingdom WT_ Wellcome Trust
Contributed Indexing: Keywords: Bayesian inference; active inference; agent-based models; animal behavior; collective motion
Entry Date(s): Date Created: 20240417 Date Completed: 20240419 Latest Revision: 20260911
Update Code: 20260911
PubMed Central ID: PMC11046639
DOI: 10.1073/pnas.2320239121
PMID: 38630721
Βάση Δεδομένων: MEDLINE
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  Data: <searchLink fieldCode="AU" term="%22Heins+C%22">Heins C</searchLink>; Department of Collective Behaviour, Max Planck Institute of Animal Behavior, Konstanz D-78457, Germany.; Centre for the Advanced Study of Collective Behaviour, University of Konstanz, Konstanz D-78457, Germany.; Department of Biology, University of Konstanz, Konstanz D-78457, Germany.; VERSES Research Lab, Los Angeles, CA 90016.<br /><searchLink fieldCode="AU" term="%22Millidge+B%22">Millidge B</searchLink>; Medical Research Council Brain Networks Dynamics Unit, University of Oxford, Oxford OX1 3TH, United Kingdom.<br /><searchLink fieldCode="AU" term="%22Da+Costa+L%22">Da Costa L</searchLink>; VERSES Research Lab, Los Angeles, CA 90016.; Department of Mathematics, Imperial College London, London SW7 2AZ, United Kingdom.; Wellcome Centre for Human Neuroimaging, University College London, London WC1N 3AR, United Kingdom.<br /><searchLink fieldCode="AU" term="%22Mann+RP%22">Mann RP</searchLink>; Department of Statistics, School of Mathematics, University of Leeds, Leeds LS2 9JT, United Kingdom.<br /><searchLink fieldCode="AU" term="%22Friston+KJ%22">Friston KJ</searchLink>; VERSES Research Lab, Los Angeles, CA 90016.; Wellcome Centre for Human Neuroimaging, University College London, London WC1N 3AR, United Kingdom.<br /><searchLink fieldCode="AU" term="%22Couzin+ID%22">Couzin ID</searchLink>; Department of Collective Behaviour, Max Planck Institute of Animal Behavior, Konstanz D-78457, Germany.; Centre for the Advanced Study of Collective Behaviour, University of Konstanz, Konstanz D-78457, Germany.; Department of Biology, University of Konstanz, Konstanz D-78457, Germany.
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  Data: <searchLink fieldCode="JN" term="%227505876%22">Proceedings of the National Academy of Sciences of the United States of America</searchLink> [Proc Natl Acad Sci U S A] 2024 Apr 23; Vol. 121 (17), pp. e2320239121. <i>Date of Electronic Publication: </i>2024 Apr 17.
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  Data: Collective motion is ubiquitous in nature; groups of animals, such as fish, birds, and ungulates appear to move as a whole, exhibiting a rich behavioral repertoire that ranges from directed movement to milling to disordered swarming. Typically, such macroscopic patterns arise from decentralized, local interactions among constituent components (e.g., individual fish in a school). Preeminent models of this process describe individuals as self-propelled particles, subject to self-generated motion and "social forces" such as short-range repulsion and long-range attraction or alignment. However, organisms are not particles; they are probabilistic decision-makers. Here, we introduce an approach to modeling collective behavior based on active inference. This cognitive framework casts behavior as the consequence of a single imperative: to minimize surprise. We demonstrate that many empirically observed collective phenomena, including cohesion, milling, and directed motion, emerge naturally when considering behavior as driven by active Bayesian inference-without explicitly building behavioral rules or goals into individual agents. Furthermore, we show that active inference can recover and generalize the classical notion of social forces as agents attempt to suppress prediction errors that conflict with their expectations. By exploring the parameter space of the belief-based model, we reveal nontrivial relationships between the individual beliefs and group properties like polarization and the tendency to visit different collective states. We also explore how individual beliefs about uncertainty determine collective decision-making accuracy. Finally, we show how agents can update their generative model over time, resulting in groups that are collectively more sensitive to external fluctuations and encode information more robustly.
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  Data: Competing interests statement:The authors declare no competing interest.
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(PMID: <searchLink fieldCode="PM" term="%2231775931%22">31775931)</searchLink><br />Neural Comput. 2017 Jan;29(1):1-49. (PMID: <searchLink fieldCode="PM" term="%2227870614%22">27870614)</searchLink><br />Sci Rep. 2019 Aug 28;9(1):12477. (PMID: <searchLink fieldCode="PM" term="%2231462661%22">31462661)</searchLink><br />Entropy (Basel). 2021 Jun 29;23(7):. (PMID: <searchLink fieldCode="PM" term="%2234210008%22">34210008)</searchLink><br />Nat Neurosci. 1999 Jan;2(1):79-87. (PMID: <searchLink fieldCode="PM" term="%2210195184%22">10195184)</searchLink><br />PLoS One. 2009 Jul 29;4(7):e6421. (PMID: <searchLink fieldCode="PM" term="%2219641614%22">19641614)</searchLink><br />Philos Trans R Soc Lond B Biol Sci. 2005 Apr 29;360(1456):815-36. (PMID: <searchLink fieldCode="PM" term="%2215937014%22">15937014)</searchLink><br />J Physiol Paris. 2006 Jul-Sep;100(1-3):70-87. (PMID: <searchLink fieldCode="PM" term="%2217097864%22">17097864)</searchLink><br />Neurosci Biobehav Rev. 2016 Sep;68:862-879. (PMID: <searchLink fieldCode="PM" term="%2227375276%22">27375276)</searchLink><br />Nat Hum Behav. 2023 Mar;7(3):430-441. (PMID: <searchLink fieldCode="PM" term="%2236864133%22">36864133)</searchLink><br />Front Behav Neurosci. 2021 Jun 24;15:647732. (PMID: <searchLink fieldCode="PM" term="%2234248515%22">34248515)</searchLink><br />Proc Natl Acad Sci U S A. 2011 Nov 15;108(46):18720-5. (PMID: <searchLink fieldCode="PM" term="%2221795604%22">21795604)</searchLink><br />Neuroimage. 2007 Jan 1;34(1):220-34. (PMID: <searchLink fieldCode="PM" term="%2217055746%22">17055746)</searchLink><br />PLoS Comput Biol. 2012;8(9):e1002678. (PMID: <searchLink fieldCode="PM" term="%2223028277%22">23028277)</searchLink><br />Sci Adv. 2020 Feb 05;6(6):eaay0792. (PMID: <searchLink fieldCode="PM" term="%2232076645%22">32076645)</searchLink><br />Proc Natl Acad Sci U S A. 2021 Dec 14;118(50):. (PMID: <searchLink fieldCode="PM" term="%2234880130%22">34880130)</searchLink><br />Philos Trans R Soc Lond B Biol Sci. 2006 Jan 29;361(1465):5-22. (PMID: <searchLink fieldCode="PM" term="%2216553306%22">16553306)</searchLink><br />Neuron. 2011 Nov 3;72(3):488-98. (PMID: <searchLink fieldCode="PM" term="%2222078508%22">22078508)</searchLink><br />J Math Psychol. 2020 Dec;99:102447. (PMID: <searchLink fieldCode="PM" term="%2233343039%22">33343039)</searchLink><br />Phys Rev E Stat Nonlin Soft Matter Phys. 2006 Aug;74(2 Pt 1):022101. (PMID: <searchLink fieldCode="PM" term="%2217025488%22">17025488)</searchLink><br />PLoS One. 2013;8(3):e58525. (PMID: <searchLink fieldCode="PM" term="%2223555585%22">23555585)</searchLink>
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  Data: <i>Keywords: </i>Bayesian inference; active inference; agent-based models; animal behavior; collective motion
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      – SubjectFull: Animals
        Type: general
      – SubjectFull: Bayes Theorem
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      – SubjectFull: Movement
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      – SubjectFull: Models, Biological
        Type: general
    Titles:
      – TitleFull: Collective behavior from surprise minimization.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Heins C
      – PersonEntity:
          Name:
            NameFull: Millidge B
      – PersonEntity:
          Name:
            NameFull: Da Costa L
      – PersonEntity:
          Name:
            NameFull: Mann RP
      – PersonEntity:
          Name:
            NameFull: Friston KJ
      – PersonEntity:
          Name:
            NameFull: Couzin ID
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 23
              M: 04
              Text: 2024 Apr 23
              Type: published
              Y: 2024
          Identifiers:
            – Type: issn-electronic
              Value: 1091-6490
          Numbering:
            – Type: volume
              Value: 121
            – Type: issue
              Value: 17
          Titles:
            – TitleFull: Proceedings of the National Academy of Sciences of the United States of America
              Type: main
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