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Xiao Y, Lei X, Zheng Z, Xiang Y, Liu YY, Peng X. Perception of motion salience shapes the emergence of collective motions. Nat Commun 2024; 15:4779. [PMID: 38839782 PMCID: PMC11153630 DOI: 10.1038/s41467-024-49151-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Accepted: 05/24/2024] [Indexed: 06/07/2024] Open
Abstract
Despite the profound implications of self-organization in animal groups for collective behaviors, understanding the fundamental principles and applying them to swarm robotics remains incomplete. Here we propose a heuristic measure of perception of motion salience (MS) to quantify relative motion changes of neighbors from first-person view. Leveraging three large bird-flocking datasets, we explore how this perception of MS relates to the structure of leader-follower (LF) relations, and further perform an individual-level correlation analysis between past perception of MS and future change rate of velocity consensus. We observe prevalence of the positive correlations in real flocks, which demonstrates that individuals will accelerate the convergence of velocity with neighbors who have higher MS. This empirical finding motivates us to introduce the concept of adaptive MS-based (AMS) interaction in swarm model. Finally, we implement AMS in a swarm of ~102 miniature robots. Swarm experiments show the significant advantage of AMS in enhancing self-organization of the swarm for smooth evacuations from confined environments.
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Affiliation(s)
- Yandong Xiao
- College of System Engineering, National University of Defense Technology, Changsha, Hunan, China.
| | - Xiaokang Lei
- College of Information and Control Engineering, Xi'an University of Architecture and Technology, Xi'an, Shaanxi, China
| | - Zhicheng Zheng
- School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an, Shaanxi, China
| | - Yalun Xiang
- School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an, Shaanxi, China
| | - Yang-Yu Liu
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Center for Artificial Intelligence and Modeling, The Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Champaign, IL, USA
| | - Xingguang Peng
- School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an, Shaanxi, China.
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2
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Yu JH, Napoli JL, Lovett-Barron M. Understanding collective behavior through neurobiology. Curr Opin Neurobiol 2024; 86:102866. [PMID: 38852986 DOI: 10.1016/j.conb.2024.102866] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Revised: 02/16/2024] [Accepted: 03/07/2024] [Indexed: 06/11/2024]
Abstract
A variety of organisms exhibit collective movement, including schooling fish and flocking birds, where coordinated behavior emerges from the interactions between group members. Despite the prevalence of collective movement in nature, little is known about the neural mechanisms producing each individual's behavior within the group. Here we discuss how a neurobiological approach can enrich our understanding of collective behavior by determining the mechanisms by which individuals interact. We provide examples of sensory systems for social communication during collective movement, highlight recent discoveries about neural systems for detecting the position and actions of social partners, and discuss opportunities for future research. Understanding the neurobiology of collective behavior can provide insight into how nervous systems function in a dynamic social world.
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Affiliation(s)
- Jo-Hsien Yu
- Department of Neurobiology, School of Biological Sciences, University of California, San Diego, La Jolla, CA, 92093, USA. https://twitter.com/anitajhyu
| | - Julia L Napoli
- Department of Neurobiology, School of Biological Sciences, University of California, San Diego, La Jolla, CA, 92093, USA. https://twitter.com/juliadoingneuro
| | - Matthew Lovett-Barron
- Department of Neurobiology, School of Biological Sciences, University of California, San Diego, La Jolla, CA, 92093, USA.
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3
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Chen Z, Zheng Y. Persistent and responsive collective motion with adaptive time delay. SCIENCE ADVANCES 2024; 10:eadk3914. [PMID: 38569026 PMCID: PMC10990279 DOI: 10.1126/sciadv.adk3914] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Accepted: 02/29/2024] [Indexed: 04/05/2024]
Abstract
It is beneficial for collective structures to simultaneously have high persistence to environmental noise and high responsivity to nontrivial external stimuli. However, without the ability to differentiate useful information from noise, there is always a tradeoff between persistence and responsivity within the collective structures. To address this, we propose adaptive time delay inspired by the adaptive behavior observed in the school of fish. This strategy is tested using particles powered by optothermal fields coupled with an optical feedback-control system. By applying the adaptive time delay with a proper threshold, we experimentally observe the responsivity of the collective structures enhanced by approximately 1.6 times without sacrificing persistence. Furthermore, we integrate adaptive time delay with long-distance transportation and obstacle-avoidance capabilities to prototype adaptive swarm microrobots. This research demonstrates the potential of adaptive time delay to address the persistence-responsivity tradeoff and lays the foundation for intelligent swarm micro/nanorobots operating in complex environments.
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Affiliation(s)
- Zhihan Chen
- Materials Science and Engineering Program and Texas Materials Institute, The University of Texas at Austin, Austin, TX 78712, USA
| | - Yuebing Zheng
- Materials Science and Engineering Program and Texas Materials Institute, The University of Texas at Austin, Austin, TX 78712, USA
- Walker Department of Mechanical Engineering, The University of Texas at Austin, Austin, TX 78712, USA
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4
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Tump AN, Deffner D, Pleskac TJ, Romanczuk P, M. Kurvers RHJ. A Cognitive Computational Approach to Social and Collective Decision-Making. PERSPECTIVES ON PSYCHOLOGICAL SCIENCE 2024; 19:538-551. [PMID: 37671891 PMCID: PMC10913326 DOI: 10.1177/17456916231186964] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/07/2023]
Abstract
Collective dynamics play a key role in everyday decision-making. Whether social influence promotes the spread of accurate information and ultimately results in adaptive behavior or leads to false information cascades and maladaptive social contagion strongly depends on the cognitive mechanisms underlying social interactions. Here we argue that cognitive modeling, in tandem with experiments that allow collective dynamics to emerge, can mechanistically link cognitive processes at the individual and collective levels. We illustrate the strength of this cognitive computational approach with two highly successful cognitive models that have been applied to interactive group experiments: evidence-accumulation and reinforcement-learning models. We show how these approaches make it possible to simultaneously study (a) how individual cognition drives social systems, (b) how social systems drive individual cognition, and (c) the dynamic feedback processes between the two layers.
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Affiliation(s)
- Alan N. Tump
- Center for Adaptive Rationality, Max Planck Institute for Human Development
- Science of Intelligence, Technische Universität Berlin
| | - Dominik Deffner
- Center for Adaptive Rationality, Max Planck Institute for Human Development
- Science of Intelligence, Technische Universität Berlin
| | | | - Pawel Romanczuk
- Science of Intelligence, Technische Universität Berlin
- Institute for Theoretical Biology, Department of Biology, Humboldt Universität zu Berlin
- Bernstein Center for Computational Neuroscience Berlin
| | - Ralf H. J. M. Kurvers
- Center for Adaptive Rationality, Max Planck Institute for Human Development
- Science of Intelligence, Technische Universität Berlin
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5
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Tump AN, Wollny-Huttarsch D, Molleman L, Kurvers RHJM. Earlier social information has a stronger influence on judgments. Sci Rep 2024; 14:105. [PMID: 38168146 PMCID: PMC10762246 DOI: 10.1038/s41598-023-50345-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Accepted: 12/19/2023] [Indexed: 01/05/2024] Open
Abstract
People's decisions are often informed by the choices of others. Evidence accumulation models provide a mechanistic account of how such social information enters the choice process. Previous research taking this approach has suggested two fundamentally different cognitive mechanisms by which people incorporate social information. On the one hand, individuals may update their evidence level instantaneously when observing social information. On the other hand, they may gradually integrate social information over time. These accounts make different predictions on how the timing of social information impacts its influence. The former predicts that timing has no impact on social information uptake. The latter predicts that social information which arrives earlier has a stronger impact because its impact increases over time. We tested both predictions in two studies in which participants first observed a perceptual stimulus. They then entered a deliberation phase in which social information arrived either early or late before reporting their judgment. In Experiment 1, early social information remained visible until the end and was thus displayed for longer than late social information. In Experiment 2, which was preregistered, early and late social information were displayed for an equal duration. In both studies, early social information had a larger impact on individuals' judgments. Further, an evidence accumulation analysis found that social information integration was best explained by both an immediate update of evidence and continuous integration over time. Because in social systems, timing plays a key role (e.g., propagation of information in social networks), our findings inform theories explaining the temporal evolution of social impact and the emergent social dynamics.
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Affiliation(s)
- Alan Novaes Tump
- Center for Adaptive Rationality, Max Planck Institute for Human Development, Berlin, Germany.
- Exzellenzcluster Science of Intelligence, Technical University Berlin, Berlin, Germany.
| | - David Wollny-Huttarsch
- Center for Adaptive Rationality, Max Planck Institute for Human Development, Berlin, Germany
| | - Lucas Molleman
- Amsterdam Brain and Cognition, University of Amsterdam, Amsterdam, Netherlands
- Tilburg School of Social and Behavioral Sciences, Tilburg University, Tilburg, Netherlands
| | - Ralf H J M Kurvers
- Center for Adaptive Rationality, Max Planck Institute for Human Development, Berlin, Germany
- Exzellenzcluster Science of Intelligence, Technical University Berlin, Berlin, Germany
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6
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Friston KJ, Parr T, Heins C, Constant A, Friedman D, Isomura T, Fields C, Verbelen T, Ramstead M, Clippinger J, Frith CD. Federated inference and belief sharing. Neurosci Biobehav Rev 2024; 156:105500. [PMID: 38056542 PMCID: PMC11139662 DOI: 10.1016/j.neubiorev.2023.105500] [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: 08/04/2023] [Revised: 11/08/2023] [Accepted: 12/01/2023] [Indexed: 12/08/2023]
Abstract
This paper concerns the distributed intelligence or federated inference that emerges under belief-sharing among agents who share a common world-and world model. Imagine, for example, several animals keeping a lookout for predators. Their collective surveillance rests upon being able to communicate their beliefs-about what they see-among themselves. But, how is this possible? Here, we show how all the necessary components arise from minimising free energy. We use numerical studies to simulate the generation, acquisition and emergence of language in synthetic agents. Specifically, we consider inference, learning and selection as minimising the variational free energy of posterior (i.e., Bayesian) beliefs about the states, parameters and structure of generative models, respectively. The common theme-that attends these optimisation processes-is the selection of actions that minimise expected free energy, leading to active inference, learning and model selection (a.k.a., structure learning). We first illustrate the role of communication in resolving uncertainty about the latent states of a partially observed world, on which agents have complementary perspectives. We then consider the acquisition of the requisite language-entailed by a likelihood mapping from an agent's beliefs to their overt expression (e.g., speech)-showing that language can be transmitted across generations by active learning. Finally, we show that language is an emergent property of free energy minimisation, when agents operate within the same econiche. We conclude with a discussion of various perspectives on these phenomena; ranging from cultural niche construction, through federated learning, to the emergence of complexity in ensembles of self-organising systems.
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Affiliation(s)
- Karl J Friston
- Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, UK; VERSES AI Research Lab, Los Angeles, CA 90016, USA.
| | - Thomas Parr
- Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, UK
| | - Conor Heins
- VERSES AI Research Lab, Los Angeles, CA 90016, USA; Department of Collective Behaviour, Max Planck Institute of Animal Behavior, 78457 Konstanz, Germany; Centre for the Advanced Study of Collective Behaviour, 78457 Konstanz, Germany; Department of Biology, University of Konstanz, 78457 Konstanz, Germany
| | - Axel Constant
- VERSES AI Research Lab, Los Angeles, CA 90016, USA; School of Engineering and Informatics, The University of Sussex, Brighton, UK
| | - Daniel Friedman
- Department of Entomology and Nematology, University of California, Davis, Davis, CA, USA; Active Inference Institute, Davis, CA 95616, USA
| | - Takuya Isomura
- Brain Intelligence Theory Unit, RIKEN Center for Brain Science, Wako, Saitama 351-0198, Japan
| | - Chris Fields
- Allen Discovery Center at Tufts University, Medford, MA 02155, USA
| | - Tim Verbelen
- VERSES AI Research Lab, Los Angeles, CA 90016, USA
| | - Maxwell Ramstead
- Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, UK; VERSES AI Research Lab, Los Angeles, CA 90016, USA
| | | | - Christopher D Frith
- Institute of Philosophy, School of Advanced Studies, University of London, UK
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7
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Hensley NM, Rivers TJ, Gerrish GA, Saha R, Oakley TH. Collective synchrony of mating signals modulated by ecological cues and social signals in bioluminescent sea fireflies. Proc Biol Sci 2023; 290:20232311. [PMID: 38018106 PMCID: PMC10685132 DOI: 10.1098/rspb.2023.2311] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Accepted: 11/06/2023] [Indexed: 11/30/2023] Open
Abstract
Individuals often employ simple rules that can emergently synchronize behaviour. Some collective behaviours are intuitively beneficial, but others like mate signalling in leks occur across taxa despite theoretical individual costs. Whether disparate instances of synchronous signalling are similarly organized is unknown, largely due to challenges observing many individuals simultaneously. Recording field collectives and ex situ playback experiments, we describe principles of synchronous bioluminescent signals produced by marine ostracods (Crustacea; Luxorina) that seem behaviorally convergent with terrestrial fireflies, and with whom they last shared a common ancestor over 500 Mya. Like synchronous fireflies, groups of signalling males use visual cues (intensity and duration of light) to decide when to signal. Individual ostracods also modulate their signal based on the distance to nearest neighbours. During peak darkness, luminescent 'waves' of synchronous displays emerge and ripple across the sea floor approximately every 60 s, but such periodicity decays within and between nights after the full moon. Our data reveal these bioluminescent aggregations are sensitive to both ecological and social light sources. Because the function of collective signals is difficult to dissect, evolutionary convergence, like in the synchronous visual displays of diverse arthropods, provides natural replicates to understand the generalities that produce emergent group behaviour.
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Affiliation(s)
- Nicholai M. Hensley
- Department of Ecology, Evolution, and Marine Biology, University of California Santa Barbara, Santa Barbara, CA 93106-9620, USA
| | - Trevor J. Rivers
- Department of Ecology and Evolutionary Biology, University of Kansas, Lawrence, KS 66405, USA
| | - Gretchen A. Gerrish
- Center for Limnology, Trout Lake Station, University of Wisconsin, Boulder Junction, Madison, WI 54512, USA
| | - Raj Saha
- Roux Institute, Northeastern University, Portland, ME 04101, USA
| | - Todd H. Oakley
- Department of Ecology, Evolution, and Marine Biology, University of California Santa Barbara, Santa Barbara, CA 93106-9620, USA
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8
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Ferrara NC, Che A, Briones B, Padilla-Coreano N, Lovett-Barron M, Opendak M. Neural Circuit Transitions Supporting Developmentally Specific Social Behavior. J Neurosci 2023; 43:7456-7462. [PMID: 37940586 PMCID: PMC10634550 DOI: 10.1523/jneurosci.1377-23.2023] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2023] [Revised: 08/01/2023] [Accepted: 08/01/2023] [Indexed: 11/10/2023] Open
Abstract
Environmentally appropriate social behavior is critical for survival across the lifespan. To support this flexible behavior, the brain must rapidly perform numerous computations taking into account sensation, memory, motor-control, and many other systems. Further complicating this process, individuals must perform distinct social behaviors adapted to the unique demands of each developmental stage; indeed, the social behaviors of the newborn would not be appropriate in adulthood and vice versa. However, our understanding of the neural circuit transitions supporting these behavioral transitions has been limited. Recent advances in neural circuit dissection tools, as well as adaptation of these tools for use at early time points, has helped uncover several novel mechanisms supporting developmentally appropriate social behavior. This review, and associated Minisymposium, bring together social neuroscience research across numerous model organisms and ages. Together, this work highlights developmentally regulated neural mechanisms and functional transitions in the roles of the sensory cortex, prefrontal cortex, amygdala, habenula, and the thalamus to support social interaction from infancy to adulthood. These studies underscore the need for synthesis across varied model organisms and across ages to advance our understanding of flexible social behavior.
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Affiliation(s)
- Nicole C Ferrara
- Discipline of Physiology and Biophysics, Chicago Medical School, Rosalind Franklin University of Medicine and Science, North Chicago, Illinois 60064
- Center for Neurobiology of Stress Resilience and Psychiatric Disorders, Rosalind Franklin University of Medicine and Science, North Chicago, Illinois 60064
| | - Alicia Che
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut 06520
| | - Brandy Briones
- Center for the Neurobiology of Addiction, Pain, and Emotion, Department of Anesthesiology and Pain Medicine, Department of Pharmacology, University of Washington, Seattle, Washington 98195
| | - Nancy Padilla-Coreano
- Evelyn F. & William McKnight Brain Institute and Department of Neuroscience, University of Florida, Gainesville, Florida 32610
| | - Matthew Lovett-Barron
- Department of Neurobiology, School of Biological Sciences, University of California San Diego, La Jolla, California 92093
| | - Maya Opendak
- Solomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205
- Kennedy Krieger Institute, Baltimore, Maryland 21205
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9
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Hansen MJ, Domenici P, Bartashevich P, Burns A, Krause J. Mechanisms of group-hunting in vertebrates. Biol Rev Camb Philos Soc 2023; 98:1687-1711. [PMID: 37199232 DOI: 10.1111/brv.12973] [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: 06/06/2022] [Revised: 04/24/2023] [Accepted: 04/26/2023] [Indexed: 05/19/2023]
Abstract
Group-hunting is ubiquitous across animal taxa and has received considerable attention in the context of its functions. By contrast much less is known about the mechanisms by which grouping predators hunt their prey. This is primarily due to a lack of experimental manipulation alongside logistical difficulties quantifying the behaviour of multiple predators at high spatiotemporal resolution as they search, select, and capture wild prey. However, the use of new remote-sensing technologies and a broadening of the focal taxa beyond apex predators provides researchers with a great opportunity to discern accurately how multiple predators hunt together and not just whether doing so provides hunters with a per capita benefit. We incorporate many ideas from collective behaviour and locomotion throughout this review to make testable predictions for future researchers and pay particular attention to the role that computer simulation can play in a feedback loop with empirical data collection. Our review of the literature showed that the breadth of predator:prey size ratios among the taxa that can be considered to hunt as a group is very large (<100 to >102 ). We therefore synthesised the literature with respect to these predator:prey ratios and found that they promoted different hunting mechanisms. Additionally, these different hunting mechanisms are also related to particular stages of the hunt (search, selection, capture) and thus we structure our review in accordance with these two factors (stage of the hunt and predator:prey size ratio). We identify several novel group-hunting mechanisms which are largely untested, particularly under field conditions, and we also highlight a range of potential study organisms that are amenable to experimental testing of these mechanisms in connection with tracking technology. We believe that a combination of new hypotheses, study systems and methodological approaches should help push the field of group-hunting in new directions.
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Affiliation(s)
- Matthew J Hansen
- Fish Biology, Fisheries and Aquaculture, Leibniz Institute of Freshwater Ecology and Inland Fisheries, Müggelseedamm 310, Berlin, 12587, Germany
| | - Paolo Domenici
- IBF-CNR, Consiglio Nazionale delle Ricerche, Area di Ricerca San Cataldo, Via G. Moruzzi No. 1, Pisa, 56124, Italy
- IAS-CNR, Località Sa Mardini, Torregrande, Oristano, 09170, Italy
| | - Palina Bartashevich
- Faculty of Life Science, Humboldt-Universität zu Berlin, Invalidenstrasse 42, Berlin, 10115, Germany
- Cluster of Excellence "Science of Intelligence," Technical University of Berlin, Marchstr. 23, Berlin, 10587, Germany
| | - Alicia Burns
- Faculty of Life Science, Humboldt-Universität zu Berlin, Invalidenstrasse 42, Berlin, 10115, Germany
- Cluster of Excellence "Science of Intelligence," Technical University of Berlin, Marchstr. 23, Berlin, 10587, Germany
| | - Jens Krause
- Fish Biology, Fisheries and Aquaculture, Leibniz Institute of Freshwater Ecology and Inland Fisheries, Müggelseedamm 310, Berlin, 12587, Germany
- Faculty of Life Science, Humboldt-Universität zu Berlin, Invalidenstrasse 42, Berlin, 10115, Germany
- Cluster of Excellence "Science of Intelligence," Technical University of Berlin, Marchstr. 23, Berlin, 10587, Germany
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10
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Connor J, Joordens M, Champion B. Fish-inspired robotic algorithm: mimicking behaviour and communication of schooling fish. BIOINSPIRATION & BIOMIMETICS 2023; 18:066007. [PMID: 37714177 DOI: 10.1088/1748-3190/acfa52] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Accepted: 09/15/2023] [Indexed: 09/17/2023]
Abstract
This study aims to present a novel flocking algorithm for robotic fish that will aid the study of fish in their natural environment. The algorithm, fish-inspired robotic algorithm (FIRA), amalgamates the standard flocking behaviors of attraction, alignment, and repulsion, together with predator avoidance, foraging, general obstacle avoidance, and wandering. The novelty of the FIRA algorithm is the combination of predictive elements to counteract processing delays from sensors and the addition of memory. Furthermore, FIRA is specifically designed to work with an indirect communication method that leads to superior performance in collision avoidance, exploration, foraging, and the emergence of realistic behaviors. By leveraging a high-latency, non-guaranteed communication methodology inspired by stigmergy methods inherent in nature, FIRA successfully addresses some of the obstacles associated with underwater communication. This breakthrough enables the realization of inexpensive, multi-agent swarms while concurrently harnessing the advantages of tetherless communication. FIRA provides a computational light control algorithm for further research with low-cost, low-computing agents. Eventually, FIRA will be used to assimilate robots into a school of biological fish, to study or influence the school. This study endeavors to demonstrate the effectiveness of FIRA by simulating it using a digital twin of a bio-inspired robotic fish. The simulation incorporates the robot's motion and sensors in a realistic, real-time environment with the algorithm used to direct the movements of individual agents. The performance of FIRA was tested against other collective flocking algorithms to determine its effectiveness. From the experiments, it was determined that FIRA outperformed the other algorithms in both collision avoidance and exploration. These experiments establish FIRA as a viable flocking algorithm to mimic fish behavior in robotics.
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Affiliation(s)
- Jack Connor
- School of Engineering, Deakin University, Geelong, Victoria 3216, Australia
| | - Matthew Joordens
- School of Engineering, Deakin University, Geelong, Victoria 3216, Australia
| | - Benjamin Champion
- School of Engineering, Deakin University, Geelong, Victoria 3216, Australia
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11
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Lei X, Xiang Y, Duan M, Peng X. Exploring the criticality hypothesis using programmable swarm robots with Vicsek-like interactions. J R Soc Interface 2023; 20:20230176. [PMID: 37464802 DOI: 10.1098/rsif.2023.0176] [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: 03/27/2023] [Accepted: 06/28/2023] [Indexed: 07/20/2023] Open
Abstract
A widely mentioned but not experimentally confirmed view (known as the 'criticality hypothesis') argues that biological swarm systems gain optimal responsiveness to perturbations and information processing capabilities by operating near the critical state where an ordered-to-disordered state transition occurs. However, various factors can induce the ordered-disordered transition, and the explicit relationship between these factors and the criticality is still unclear. Here, we present an experimental validation for the criticality hypothesis by employing real programmable swarm-robotic systems (up to 50 robots) governed by Vicsek-like interactions, subject to time-varying stimulus-response and hazard avoidance. We find that (i) not all ordered-disordered motion transitions correspond to the functional advantages for groups; (ii) collective response of groups is maximized near the critical state induced by alignment weight or scale rather than noise and other non-alignment factors; and (iii) those non-alignment factors act to highlight the functional advantages of alignment-induced criticality. These results suggest that the adjustability of velocity or directional coupling between individuals plays an essential role in the acquisition of maximizing collective response by criticality. Our results contribute to understanding the adjustment strategies of animal interactions from a perspective of criticality and provide insights into the design and control of swarm robotics.
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Affiliation(s)
- Xiaokang Lei
- College of Information and Control Engineering, Xi'an University of Architecture and Technology, Xi'an 710055, People's Republic of China
| | - Yalun Xiang
- College of Information and Control Engineering, Xi'an University of Architecture and Technology, Xi'an 710055, People's Republic of China
| | - Mengyuan Duan
- College of Information and Control Engineering, Xi'an University of Architecture and Technology, Xi'an 710055, People's Republic of China
| | - Xingguang Peng
- School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an 710072, People's Republic of China
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12
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Sridhar VH, Davidson JD, Twomey CR, Sosna MMG, Nagy M, Couzin ID. Inferring social influence in animal groups across multiple timescales. Philos Trans R Soc Lond B Biol Sci 2023; 378:20220062. [PMID: 36802787 PMCID: PMC9939267 DOI: 10.1098/rstb.2022.0062] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/21/2023] Open
Abstract
Many animal behaviours exhibit complex temporal dynamics, suggesting there are multiple timescales at which they should be studied. However, researchers often focus on behaviours that occur over relatively restricted temporal scales, typically ones that are more accessible to human observation. The situation becomes even more complex when considering multiple animals interacting, where behavioural coupling can introduce new timescales of importance. Here, we present a technique to study the time-varying nature of social influence in mobile animal groups across multiple temporal scales. As case studies, we analyse golden shiner fish and homing pigeons, which move in different media. By analysing pairwise interactions among individuals, we show that predictive power of the factors affecting social influence depends on the timescale of analysis. Over short timescales the relative position of a neighbour best predicts its influence and the distribution of influence across group members is relatively linear, with a small slope. At longer timescales, however, both relative position and kinematics are found to predict influence, and nonlinearity in the influence distribution increases, with a small number of individuals being disproportionately influential. Our results demonstrate that different interpretations of social influence arise from analysing behaviour at different timescales, highlighting the importance of considering its multiscale nature. This article is part of a discussion meeting issue 'Collective behaviour through time'.
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Affiliation(s)
- Vivek H. Sridhar
- Department of Biology, University of Konstanz, 78464 Konstanz, Germany,Centre for the Advanced Study of Collective Behaviour, University of Konstanz, 78464 Konstanz, Germany,Department of Collective Behaviour, Max Planck Institute of Animal Behavior, 78464 Konstanz, Germany,Department for the Ecology of Animal Societies, Max Planck Institute of Animal Behavior, 78467 Konstanz, Germany
| | - Jacob D. Davidson
- Department of Biology, University of Konstanz, 78464 Konstanz, Germany,Centre for the Advanced Study of Collective Behaviour, University of Konstanz, 78464 Konstanz, Germany,Department of Collective Behaviour, Max Planck Institute of Animal Behavior, 78464 Konstanz, Germany
| | - Colin R. Twomey
- Department of Biology, University of Pennsylvania, Philadelphia, PA 19104, USA,Mind Center for Outreach, Research, and Education, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Matthew M. G. Sosna
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544, USA
| | - Máté Nagy
- Department of Biology, University of Konstanz, 78464 Konstanz, Germany,Centre for the Advanced Study of Collective Behaviour, University of Konstanz, 78464 Konstanz, Germany,Department of Collective Behaviour, Max Planck Institute of Animal Behavior, 78464 Konstanz, Germany,MTA-ELTE Statistical and Biological Physics Research Group, Hungarian Academy of Sciences, Budapest 1117, Hungary,MTA-ELTE ‘Lendület’ Collective Behaviour Research Group, Hungarian Academy of Sciences, Eötvös Loránd University, Budapest 1117, Hungary,Department of Biological Physics, Eötvös Loránd University, Pázmány Péter sétány 1A, Budapest 1117, Hungary
| | - Iain D. Couzin
- Department of Biology, University of Konstanz, 78464 Konstanz, Germany,Centre for the Advanced Study of Collective Behaviour, University of Konstanz, 78464 Konstanz, Germany,Department of Collective Behaviour, Max Planck Institute of Animal Behavior, 78464 Konstanz, Germany
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13
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Fahimipour AK, Gil MA, Celis MR, Hein GF, Martin BT, Hein AM. Wild animals suppress the spread of socially transmitted misinformation. Proc Natl Acad Sci U S A 2023; 120:e2215428120. [PMID: 36976767 PMCID: PMC10083541 DOI: 10.1073/pnas.2215428120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Accepted: 02/07/2023] [Indexed: 03/29/2023] Open
Abstract
Understanding the mechanisms by which information and misinformation spread through groups of individual actors is essential to the prediction of phenomena ranging from coordinated group behaviors to misinformation epidemics. Transmission of information through groups depends on the rules that individuals use to transform the perceived actions of others into their own behaviors. Because it is often not possible to directly infer decision-making strategies in situ, most studies of behavioral spread assume that individuals make decisions by pooling or averaging the actions or behavioral states of neighbors. However, whether individuals may instead adopt more sophisticated strategies that exploit socially transmitted information, while remaining robust to misinformation, is unknown. Here, we study the relationship between individual decision-making and misinformation spread in groups of wild coral reef fish, where misinformation occurs in the form of false alarms that can spread contagiously through groups. Using automated visual field reconstruction of wild animals, we infer the precise sequences of socially transmitted visual stimuli perceived by individuals during decision-making. Our analysis reveals a feature of decision-making essential for controlling misinformation spread: dynamic adjustments in sensitivity to socially transmitted cues. This form of dynamic gain control can be achieved by a simple and biologically widespread decision-making circuit, and it renders individual behavior robust to natural fluctuations in misinformation exposure.
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Affiliation(s)
- Ashkaan K. Fahimipour
- Department of Biological Sciences, Florida Atlantic University, Boca Raton, FL33431
- Institute of Marine Sciences, University of California Santa Cruz, Santa Cruz, CA95060
| | - Michael A. Gil
- Department of Ecology & Evolutionary Biology, University of Colorado Boulder, Boulder, CO80309
| | - Maria Rosa Celis
- Institute of Marine Sciences, University of California Santa Cruz, Santa Cruz, CA95060
| | | | - Benjamin T. Martin
- Institute for Biodiversity & Ecosystem Dynamics, University of Amsterdam, 1090GE Amsterdam, The Netherlands
| | - Andrew M. Hein
- Department of Computational Biology, Cornell University, Ithaca, NY14850
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14
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Benvegnen B, Chaté H, Krapivsky PL, Tailleur J, Solon A. Flocking in one dimension: Asters and reversals. Phys Rev E 2022; 106:054608. [PMID: 36559354 DOI: 10.1103/physreve.106.054608] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 10/23/2022] [Indexed: 06/17/2023]
Abstract
We study the one-dimensional active Ising model in which aligning particles undergo diffusion biased by the signs of their spins. The phase diagram obtained varying the density of particles, their hopping rate, and the temperature controlling the alignment shows a homogeneous disordered phase but no homogeneous ordered one, as well as two phases with localized dense structures. In the flocking phase, large ordered aggregates move ballistically and stochastically reverse their direction of motion. In what we termed the "aster" phase, dense immobile aggregates of opposite magnetization face each other, exchanging particles, without any net motion of the aggregates. Using a combination of numerical simulations and mean-field theory, we study the evolution of the shapes of the flocks, the statistics of their reversal times, and their coarsening dynamics. Solving exactly for the zero-temperature dynamics of an aster allows us to understand their coarsening, which shows extremal dynamics, while mean-field equations account for their shape.
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Affiliation(s)
- Brieuc Benvegnen
- Sorbonne Université, CNRS, Laboratoire de Physique Théorique de la Matière Condensée, 75005 Paris, France
| | - Hugues Chaté
- Sorbonne Université, CNRS, Laboratoire de Physique Théorique de la Matière Condensée, 75005 Paris, France
- Service de Physique de l'Etat Condensé, CEA, CNRS Université Paris-Saclay, CEA-Saclay, 91191 Gif-sur-Yvette, France
- Computational Science Research Center, Beijing 100094, China
| | - Pavel L Krapivsky
- Department of Physics, Boston University, Boston, Massachusetts 02215, USA
- Santa Fe Institute, Santa Fe, New Mexico 87501, USA
| | - Julien Tailleur
- Université Paris Cité, Laboratoire Matière et Systèmes Complexes (MSC), UMR 7057 CNRS, F-75205 Paris, France
| | - Alexandre Solon
- Sorbonne Université, CNRS, Laboratoire de Physique Théorique de la Matière Condensée, 75005 Paris, France
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15
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Khajehabdollahi S, Prosi J, Giannakakis E, Martius G, Levina A. When to Be Critical? Performance and Evolvability in Different Regimes of Neural Ising Agents. ARTIFICIAL LIFE 2022; 28:458-478. [PMID: 35984417 DOI: 10.1162/artl_a_00383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
It has long been hypothesized that operating close to the critical state is beneficial for natural and artificial evolutionary systems. We put this hypothesis to test in a system of evolving foraging agents controlled by neural networks that can adapt the agents' dynamical regime throughout evolution. Surprisingly, we find that all populations that discover solutions evolve to be subcritical. By a resilience analysis, we find that there are still benefits of starting the evolution in the critical regime. Namely, initially critical agents maintain their fitness level under environmental changes (for example, in the lifespan) and degrade gracefully when their genome is perturbed. At the same time, initially subcritical agents, even when evolved to the same fitness, are often inadequate to withstand the changes in the lifespan and degrade catastrophically with genetic perturbations. Furthermore, we find the optimal distance to criticality depends on the task complexity. To test it we introduce a hard task and a simple task: For the hard task, agents evolve closer to criticality, whereas more subcritical solutions are found for the simple task. We verify that our results are independent of the selected evolutionary mechanisms by testing them on two principally different approaches: a genetic algorithm and an evolutionary strategy. In summary, our study suggests that although optimal behaviour in the simple task is obtained in a subcritical regime, initializing near criticality is important to be efficient at finding optimal solutions for new tasks of unknown complexity.
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Affiliation(s)
- Sina Khajehabdollahi
- University of Tübingen, Department of Computer Science
- Max Planck Institute for Biological Cybernetics.
| | - Jan Prosi
- University of Tübingen, Department of Computer Science
- Max Planck Institute for Biological Cybernetics
| | - Emmanouil Giannakakis
- University of Tübingen, Department of Computer Science
- Max Planck Institute for Biological Cybernetics
| | | | - Anna Levina
- University of Tübingen, Department of Computer Science
- Max Planck Institute for Biological Cybernetics
- Bernstein Center for Computational Neuroscience Tübingen
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