1
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Kao AB, Banerjee SC, Francisco FA, Berdahl AM. Timing decisions as the next frontier for collective intelligence. Trends Ecol Evol 2024:S0169-5347(24)00141-1. [PMID: 38964933 DOI: 10.1016/j.tree.2024.06.003] [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: 12/11/2023] [Revised: 06/04/2024] [Accepted: 06/06/2024] [Indexed: 07/06/2024]
Abstract
The past decade has witnessed a growing interest in collective decision making, particularly the idea that groups can make more accurate decisions compared with individuals. However, nearly all research to date has focused on spatial decisions (e.g., food patches). Here, we highlight the equally important, but severely understudied, realm of temporal collective decision making (i.e., decisions about when to perform an action). We illustrate differences between temporal and spatial decisions, including the irreversibility of time, cost asymmetries, the speed-accuracy tradeoff, and game theoretic dynamics. Given these fundamental differences, temporal collective decision making likely requires different mechanisms to generate collective intelligence. Research focused on temporal decisions should lead to an expanded understanding of the adaptiveness and constraints of living in groups.
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Affiliation(s)
- Albert B Kao
- Department of Biology, University of Massachusetts Boston, Boston, MA 02125, USA.
| | | | - Fritz A Francisco
- Department of Biology, University of Massachusetts Boston, Boston, MA 02125, USA.
| | - Andrew M Berdahl
- School of Aquatic and Fishery Sciences, University of Washington, Seattle, WA 98195, USA.
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2
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Kuntz G, Huang J, Rask M, Lindgren-Ruby A, Shinsato JY, Bi D, Tabatabai AP. Spatial confinement affects the heterogeneity and interactions between shoaling fish. Sci Rep 2024; 14:12296. [PMID: 38811673 DOI: 10.1038/s41598-024-63245-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Accepted: 05/27/2024] [Indexed: 05/31/2024] Open
Abstract
Living objects are able to consume chemical energy and process information independently from others. However, living objects can coordinate to form ordered groups such as schools of fish. This work considers these complex groups as living materials and presents imaging-based experiments of laboratory schools of fish to understand how activity, which is a non-equilibrium feature, affects the structure and dynamics of a group. We use spatial confinement to control the motion and structure of fish within quasi-2D shoals of fish and use image analysis techniques to make quantitative observations of the structures, their spatial heterogeneity, and their temporal fluctuations. Furthermore, we utilize Monte Carlo simulations to replicate the experimentally observed data which provides insight into the effective interactions between fish and confirms the presence of a confinement-based behavioral preference transition. In addition, unlike in short-range interacting systems, here structural heterogeneity and dynamic activities are positively correlated as a result of complex interplay between spatial arrangement and behavioral dynamics in fish collectives.
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Affiliation(s)
- Gabriel Kuntz
- Department of Physics, Seattle University, Seattle, WA, 98122, USA
| | - Junxiang Huang
- Department of Physics, Northeastern University, Boston, MA, 02115, USA
| | - Mitchell Rask
- Department of Physics, Seattle University, Seattle, WA, 98122, USA
| | | | - Jacob Y Shinsato
- Department of Physics, Seattle University, Seattle, WA, 98122, USA
| | - Dapeng Bi
- Department of Physics, Northeastern University, Boston, MA, 02115, USA
| | - A Pasha Tabatabai
- Department of Physics, Seattle University, Seattle, WA, 98122, USA.
- Physics Department, California Polytechnic State University, San Luis Obispo, CA, 93410, USA.
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3
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Amichay G, Li L, Nagy M, Couzin ID. Revealing the mechanism and function underlying pairwise temporal coupling in collective motion. Nat Commun 2024; 15:4356. [PMID: 38778073 PMCID: PMC11111445 DOI: 10.1038/s41467-024-48458-z] [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: 06/16/2023] [Accepted: 04/29/2024] [Indexed: 05/25/2024] Open
Abstract
Coordinated motion in animal groups has predominantly been studied with a focus on spatial interactions, such as how individuals position and orient themselves relative to one another. Temporal aspects have, by contrast, received much less attention. Here, by studying pairwise interactions in juvenile zebrafish (Danio rerio)-including using immersive volumetric virtual reality (VR) with which we can directly test models of social interactions in situ-we reveal that there exists a rhythmic out-of-phase (i.e., an alternating) temporal coordination dynamic. We find that reciprocal (bi-directional) feedback is both necessary and sufficient to explain this emergent coupling. Beyond a mechanistic understanding, we find, both from VR experiments and analysis of freely swimming pairs, that temporal coordination considerably improves spatial responsiveness, such as to changes in the direction of motion of a partner. Our findings highlight the synergistic role of spatial and temporal coupling in facilitating effective communication between individuals on the move.
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Affiliation(s)
- Guy Amichay
- Centre for the Advanced Study of Collective Behaviour, University of Konstanz, Universitätsstraße 10, 78464, Konstanz, Germany.
- Department of Collective Behaviour, Max-Planck Institute of Animal Behavior, Konstanz, Germany.
- Department of Biology, University of Konstanz, Konstanz, Germany.
- Department of Engineering Sciences and Applied Mathematics, Northwestern University, Evanston, IL, USA.
- Northwestern Institute on Complex Systems, Northwestern University, Evanston, IL, USA.
| | - Liang Li
- Centre for the Advanced Study of Collective Behaviour, University of Konstanz, Universitätsstraße 10, 78464, Konstanz, Germany
- Department of Collective Behaviour, Max-Planck Institute of Animal Behavior, Konstanz, Germany
- Department of Biology, University of Konstanz, Konstanz, Germany
| | - Máté Nagy
- Centre for the Advanced Study of Collective Behaviour, University of Konstanz, Universitätsstraße 10, 78464, Konstanz, Germany.
- Department of Collective Behaviour, Max-Planck Institute of Animal Behavior, Konstanz, Germany.
- Department of Biology, University of Konstanz, Konstanz, Germany.
- MTA-ELTE Lendület Collective Behaviour Research Group, Hungarian Academy of Sciences, Budapest, Hungary.
- ELTE Eötvös Loránd University, Department of Biological Physics, Budapest, Hungary.
| | - Iain D Couzin
- Centre for the Advanced Study of Collective Behaviour, University of Konstanz, Universitätsstraße 10, 78464, Konstanz, Germany.
- Department of Collective Behaviour, Max-Planck Institute of Animal Behavior, Konstanz, Germany.
- Department of Biology, University of Konstanz, Konstanz, Germany.
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4
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Puy A, Gimeno E, Torrents J, Bartashevich P, Miguel MC, Pastor-Satorras R, Romanczuk P. Selective social interactions and speed-induced leadership in schooling fish. Proc Natl Acad Sci U S A 2024; 121:e2309733121. [PMID: 38662546 PMCID: PMC11067465 DOI: 10.1073/pnas.2309733121] [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: 06/12/2023] [Accepted: 03/27/2024] [Indexed: 05/05/2024] Open
Abstract
Animals moving together in groups are believed to interact among each other with effective social forces, such as attraction, repulsion, and alignment. Such forces can be inferred using "force maps," i.e., by analyzing the dependency of the acceleration of a focal individual on relevant variables. Here, we introduce a force map technique suitable for the analysis of the alignment forces experienced by individuals. After validating it using an agent-based model, we apply the force map to experimental data of schooling fish. We observe signatures of an effective alignment force with faster neighbors and an unexpected antialignment with slower neighbors. Instead of an explicit antialignment behavior, we suggest that the observed pattern is the result of a selective attention mechanism, where fish pay less attention to slower neighbors. This mechanism implies the existence of temporal leadership interactions based on relative speeds between neighbors. We present support for this hypothesis both from agent-based modeling as well as from exploring leader-follower relationships in the experimental data.
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Affiliation(s)
- Andreu Puy
- Departament de Física, Universitat Politècnica de Catalunya, Barcelona08034, Spain
| | - Elisabet Gimeno
- Departament de Física, Universitat Politècnica de Catalunya, Barcelona08034, Spain
- Departament de Física de la Matèria Condensada, Universitat de Barcelona, Barcelona08028, Spain
| | - Jordi Torrents
- Departament de Física, Universitat Politècnica de Catalunya, Barcelona08034, Spain
- Departament de Física de la Matèria Condensada, Universitat de Barcelona, Barcelona08028, Spain
| | - Palina Bartashevich
- Institute for Theoretical Biology, Department of Biology, Humboldt-Universität zu Berlin, Berlin10115, Germany
- Excellence Cluster Science of Intelligence, Technische Universität Berlin, Berlin10587, Germany
| | - M. Carmen Miguel
- Departament de Física de la Matèria Condensada, Universitat de Barcelona, Barcelona08028, Spain
- Institute of Complex Systems (UBICS), Universitat de Barcelona, Barcelona08028, Spain
| | | | - Pawel Romanczuk
- Institute for Theoretical Biology, Department of Biology, Humboldt-Universität zu Berlin, Berlin10115, Germany
- Excellence Cluster Science of Intelligence, Technische Universität Berlin, Berlin10587, Germany
- Bernstein Center for Computational Neuroscience, Berlin10115, Germany
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5
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Heins C, Millidge B, Da Costa L, Mann RP, Friston KJ, Couzin ID. Collective behavior from surprise minimization. Proc Natl Acad Sci U S A 2024; 121:e2320239121. [PMID: 38630721 PMCID: PMC11046639 DOI: 10.1073/pnas.2320239121] [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: 11/27/2023] [Accepted: 03/08/2024] [Indexed: 04/19/2024] Open
Abstract
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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Affiliation(s)
- Conor Heins
- Department of Collective Behaviour, Max Planck Institute of Animal Behavior, KonstanzD-78457, Germany
- Centre for the Advanced Study of Collective Behaviour, University of Konstanz, KonstanzD-78457, Germany
- Department of Biology, University of Konstanz, KonstanzD-78457, Germany
- VERSES Research Lab, Los Angeles, CA90016
| | - Beren Millidge
- Medical Research Council Brain Networks Dynamics Unit, University of Oxford, OxfordOX1 3TH, United Kingdom
| | - Lancelot Da Costa
- VERSES Research Lab, Los Angeles, CA90016
- Department of Mathematics, Imperial College London, LondonSW7 2AZ, United Kingdom
- Wellcome Centre for Human Neuroimaging, University College London, LondonWC1N 3AR, United Kingdom
| | - Richard P. Mann
- Department of Statistics, School of Mathematics, University of Leeds, LeedsLS2 9JT, United Kingdom
| | - Karl J. Friston
- VERSES Research Lab, Los Angeles, CA90016
- Wellcome Centre for Human Neuroimaging, University College London, LondonWC1N 3AR, United Kingdom
| | - Iain D. Couzin
- Department of Collective Behaviour, Max Planck Institute of Animal Behavior, KonstanzD-78457, Germany
- Centre for the Advanced Study of Collective Behaviour, University of Konstanz, KonstanzD-78457, Germany
- Department of Biology, University of Konstanz, KonstanzD-78457, Germany
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6
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McMillen P, Levin M. Collective intelligence: A unifying concept for integrating biology across scales and substrates. Commun Biol 2024; 7:378. [PMID: 38548821 PMCID: PMC10978875 DOI: 10.1038/s42003-024-06037-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Accepted: 03/11/2024] [Indexed: 04/01/2024] Open
Abstract
A defining feature of biology is the use of a multiscale architecture, ranging from molecular networks to cells, tissues, organs, whole bodies, and swarms. Crucially however, biology is not only nested structurally, but also functionally: each level is able to solve problems in distinct problem spaces, such as physiological, morphological, and behavioral state space. Percolating adaptive functionality from one level of competent subunits to a higher functional level of organization requires collective dynamics: multiple components must work together to achieve specific outcomes. Here we overview a number of biological examples at different scales which highlight the ability of cellular material to make decisions that implement cooperation toward specific homeodynamic endpoints, and implement collective intelligence by solving problems at the cell, tissue, and whole-organism levels. We explore the hypothesis that collective intelligence is not only the province of groups of animals, and that an important symmetry exists between the behavioral science of swarms and the competencies of cells and other biological systems at different scales. We then briefly outline the implications of this approach, and the possible impact of tools from the field of diverse intelligence for regenerative medicine and synthetic bioengineering.
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Affiliation(s)
- Patrick McMillen
- Department of Biology, Tufts University, Medford, MA, 02155, USA
- Allen Discovery Center at Tufts University, Medford, MA, 02155, USA
| | - Michael Levin
- Department of Biology, Tufts University, Medford, MA, 02155, USA.
- Allen Discovery Center at Tufts University, Medford, MA, 02155, USA.
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, 02115, USA.
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7
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Beetz MJ. A perspective on neuroethology: what the past teaches us about the future of neuroethology. J Comp Physiol A Neuroethol Sens Neural Behav Physiol 2024; 210:325-346. [PMID: 38411712 PMCID: PMC10995053 DOI: 10.1007/s00359-024-01695-5] [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: 12/13/2023] [Revised: 02/12/2024] [Accepted: 02/13/2024] [Indexed: 02/28/2024]
Abstract
For 100 years, the Journal of Comparative Physiology-A has significantly supported research in the field of neuroethology. The celebration of the journal's centennial is a great time point to appreciate the recent progress in neuroethology and to discuss possible avenues of the field. Animal behavior is the main source of inspiration for neuroethologists. This is illustrated by the huge diversity of investigated behaviors and species. To explain behavior at a mechanistic level, neuroethologists combine neuroscientific approaches with sophisticated behavioral analysis. The rapid technological progress in neuroscience makes neuroethology a highly dynamic and exciting field of research. To summarize the recent scientific progress in neuroethology, I went through all abstracts of the last six International Congresses for Neuroethology (ICNs 2010-2022) and categorized them based on the sensory modalities, experimental model species, and research topics. This highlights the diversity of neuroethology and gives us a perspective on the field's scientific future. At the end, I highlight three research topics that may, among others, influence the future of neuroethology. I hope that sharing my roots may inspire other scientists to follow neuroethological approaches.
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Affiliation(s)
- M Jerome Beetz
- Zoology II, Biocenter, University of Würzburg, 97074, Würzburg, Germany.
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8
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Warren WH, Falandays JB, Yoshida K, Wirth TD, Free BA. Human Crowds as Social Networks: Collective Dynamics of Consensus and Polarization. PERSPECTIVES ON PSYCHOLOGICAL SCIENCE 2024; 19:522-537. [PMID: 37526132 PMCID: PMC10830891 DOI: 10.1177/17456916231186406] [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: 08/02/2023]
Abstract
A ubiquitous type of collective behavior and decision-making is the coordinated motion of bird flocks, fish schools, and human crowds. Collective decisions to move in the same direction, turn right or left, or split into subgroups arise in a self-organized fashion from local interactions between individuals without central plans or designated leaders. Strikingly similar phenomena of consensus (collective motion), clustering (subgroup formation), and bipolarization (splitting into extreme groups) are also observed in opinion formation. As we developed models of crowd dynamics and analyzed crowd networks, we found ourselves going down the same path as models of opinion dynamics in social networks. In this article, we draw out the parallels between human crowds and social networks. We show that models of crowd dynamics and opinion dynamics have a similar mathematical form and generate analogous phenomena in multiagent simulations. We suggest that they can be unified by a common collective dynamics, which may be extended to other psychological collectives. Models of collective dynamics thus offer a means to account for collective behavior and collective decisions without appealing to a priori mental structures.
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Affiliation(s)
- William H Warren
- Department of Cognitive, Linguistic, and Psychological Sciences, Brown University
| | - J Benjamin Falandays
- Department of Cognitive, Linguistic, and Psychological Sciences, Brown University
| | - Kei Yoshida
- Department of Cognitive, Linguistic, and Psychological Sciences, Brown University
| | - Trenton D Wirth
- Department of Cognitive, Linguistic, and Psychological Sciences, Brown University
| | - Brian A Free
- Department of Cognitive, Linguistic, and Psychological Sciences, Brown University
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9
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Saintyves B, Spenko M, Jaeger HM. A self-organizing robotic aggregate using solid and liquid-like collective states. Sci Robot 2024; 9:eadh4130. [PMID: 38266100 DOI: 10.1126/scirobotics.adh4130] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Accepted: 12/19/2023] [Indexed: 01/26/2024]
Abstract
Designing robotic systems that can change their physical form factor as well as their compliance to adapt to environmental constraints remains a major conceptual and technical challenge. To address this, we introduce the Granulobot, a modular system that blurs the distinction between soft, modular, and swarm robotics. The system consists of gear-like units that each contain a single actuator such that units can self-assemble into larger, granular aggregates using magnetic coupling. These aggregates can reconfigure dynamically and also split into subsystems that might later recombine. Aggregates can self-organize into collective states with solid- and liquid-like properties, thus displaying widely differing compliance. These states can be perturbed locally via actuators or externally via mechanical feedback from the environment to produce adaptive shape-shifting in a decentralized manner. This, in turn, can generate locomotion strategies adapted to different conditions. Aggregates can move over obstacles without using external sensors or coordinates to maintain a steady gait over different surfaces without electronic communication among units. The modular design highlights a physical, morphological form of control that advances the development of resilient robotic systems with the ability to morph and adapt to different functions and conditions.
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Affiliation(s)
| | - Matthew Spenko
- Mechanical, Materials, and Aerospace Engineering, Illinois Institute of Technology, Chicago, IL 60616, USA
| | - Heinrich M Jaeger
- James Franck Institute, University of Chicago, Chicago, IL 60637, USA
- Department of Physics, University of Chicago, Chicago, IL 60637, USA
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10
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Papageorgiou D, Nyaguthii B, Farine DR. Compromise or choose: shared movement decisions in wild vulturine guineafowl. Commun Biol 2024; 7:95. [PMID: 38218910 PMCID: PMC10787764 DOI: 10.1038/s42003-024-05782-w] [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: 05/31/2023] [Accepted: 01/04/2024] [Indexed: 01/15/2024] Open
Abstract
Shared-decision making is beneficial for the maintenance of group-living. However, little is known about whether consensus decision-making follows similar processes across different species. Addressing this question requires robust quantification of how individuals move relative to each other. Here we use high-resolution GPS-tracking of two vulturine guineafowl (Acryllium vulturinum) groups to test the predictions from a classic theoretical model of collective motion. We show that, in both groups, all individuals can successfully initiate directional movements, although males are more likely to be followed than females. When multiple group members initiate simultaneously, follower decisions depend on directional agreement, with followers compromising directions if the difference between them is small or choosing the majority direction if the difference is large. By aligning with model predictions and replicating the findings of a previous field study on olive baboons (Papio anubis), our results suggest that a common process governs collective decision-making in moving animal groups.
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Affiliation(s)
- Danai Papageorgiou
- University of Zurich, Department of Evolutionary Biology and Environmental Studies, Winterthurerstrasse 190, 8057, Zurich, Switzerland.
- Max Planck Institute of Animal Behavior, Department of Collective Behavior, Universitätsstraße 10, Konstanz, 78457, Germany.
- University of Konstanz, Department of Biology, Universitätsstraße 10, Konstanz, 78457, Germany.
- Kenya Wildlife Service, P.O. Box 40241-001000, Nairobi, Kenya.
- Wissenschaftskolleg zu Berlin, College for Life Sciences, Wallotstrasse 19, Berlin, 14193, Germany.
| | - Brendah Nyaguthii
- University of Eldoret, School of Natural Resource Management, Department of Wildlife, 1125-30100, Eldoret, Kenya
- Mpala Research Centre, P.O. Box 92, Nanyuki, 10400, Kenya
- National Museums of Kenya, Department of Ornithology, P.O. Box 40658-001000, Nairobi, Kenya
| | - Damien R Farine
- University of Zurich, Department of Evolutionary Biology and Environmental Studies, Winterthurerstrasse 190, 8057, Zurich, Switzerland.
- Max Planck Institute of Animal Behavior, Department of Collective Behavior, Universitätsstraße 10, Konstanz, 78457, Germany.
- National Museums of Kenya, Department of Ornithology, P.O. Box 40658-001000, Nairobi, Kenya.
- Australian National University, Division of Ecology and Evolution, Research School of Biology, 46 Sullivans Creek Road, Canberra, ACT, 2600, Australia.
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11
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Priorelli M, Pezzulo G, Stoianov IP. Deep kinematic inference affords efficient and scalable control of bodily movements. Proc Natl Acad Sci U S A 2023; 120:e2309058120. [PMID: 38085784 PMCID: PMC10743426 DOI: 10.1073/pnas.2309058120] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Accepted: 10/24/2023] [Indexed: 12/18/2023] Open
Abstract
Performing goal-directed movements requires mapping goals from extrinsic (workspace-relative) to intrinsic (body-relative) coordinates and then to motor signals. Mainstream approaches based on optimal control realize the mappings by minimizing cost functions, which is computationally demanding. Instead, active inference uses generative models to produce sensory predictions, which allows a cheaper inversion to the motor signals. However, devising generative models to control complex kinematic chains like the human body is challenging. We introduce an active inference architecture that affords a simple but effective mapping from extrinsic to intrinsic coordinates via inference and easily scales up to drive complex kinematic chains. Rich goals can be specified in both intrinsic and extrinsic coordinates using attractive or repulsive forces. The proposed model reproduces sophisticated bodily movements and paves the way for computationally efficient and biologically plausible control of actuated systems.
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Affiliation(s)
- Matteo Priorelli
- National Research Council, Institute of Cognitive Sciences and Technologies, Padova35137, Italy
| | - Giovanni Pezzulo
- National Research Council, Institute of Cognitive Sciences and Technologies, Rome00185, Italy
| | - Ivilin Peev Stoianov
- National Research Council, Institute of Cognitive Sciences and Technologies, Padova35137, Italy
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12
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Williams HJ, Sridhar VH, Hurme E, Gall GE, Borrego N, Finerty GE, Couzin ID, Galizia CG, Dominy NJ, Rowland HM, Hauber ME, Higham JP, Strandburg-Peshkin A, Melin AD. Sensory collectives in natural systems. eLife 2023; 12:e88028. [PMID: 38019274 PMCID: PMC10686622 DOI: 10.7554/elife.88028] [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: 04/03/2023] [Accepted: 11/10/2023] [Indexed: 11/30/2023] Open
Abstract
Groups of animals inhabit vastly different sensory worlds, or umwelten, which shape fundamental aspects of their behaviour. Yet the sensory ecology of species is rarely incorporated into the emerging field of collective behaviour, which studies the movements, population-level behaviours, and emergent properties of animal groups. Here, we review the contributions of sensory ecology and collective behaviour to understanding how animals move and interact within the context of their social and physical environments. Our goal is to advance and bridge these two areas of inquiry and highlight the potential for their creative integration. To achieve this goal, we organise our review around the following themes: (1) identifying the promise of integrating collective behaviour and sensory ecology; (2) defining and exploring the concept of a 'sensory collective'; (3) considering the potential for sensory collectives to shape the evolution of sensory systems; (4) exploring examples from diverse taxa to illustrate neural circuits involved in sensing and collective behaviour; and (5) suggesting the need for creative conceptual and methodological advances to quantify 'sensescapes'. In the final section, (6) applications to biological conservation, we argue that these topics are timely, given the ongoing anthropogenic changes to sensory stimuli (e.g. via light, sound, and chemical pollution) which are anticipated to impact animal collectives and group-level behaviour and, in turn, ecosystem composition and function. Our synthesis seeks to provide a forward-looking perspective on how sensory ecologists and collective behaviourists can both learn from and inspire one another to advance our understanding of animal behaviour, ecology, adaptation, and evolution.
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Affiliation(s)
- Hannah J Williams
- Max Planck Institute of Animal BehaviorKonstanzGermany
- Centre for the Advanced Study of Collective Behaviour, University of KonstanzKonstanzGermany
- Biology Department, University of KonstanzKonstanzGermany
| | - Vivek H Sridhar
- Max Planck Institute of Animal BehaviorKonstanzGermany
- Centre for the Advanced Study of Collective Behaviour, University of KonstanzKonstanzGermany
- Biology Department, University of KonstanzKonstanzGermany
| | - Edward Hurme
- Max Planck Institute of Animal BehaviorKonstanzGermany
- Centre for the Advanced Study of Collective Behaviour, University of KonstanzKonstanzGermany
- Biology Department, University of KonstanzKonstanzGermany
| | - Gabriella E Gall
- Max Planck Institute of Animal BehaviorKonstanzGermany
- Centre for the Advanced Study of Collective Behaviour, University of KonstanzKonstanzGermany
- Biology Department, University of KonstanzKonstanzGermany
- Zukunftskolleg, University of KonstanzKonstanzGermany
| | | | | | - Iain D Couzin
- Max Planck Institute of Animal BehaviorKonstanzGermany
- Centre for the Advanced Study of Collective Behaviour, University of KonstanzKonstanzGermany
- Biology Department, University of KonstanzKonstanzGermany
| | - C Giovanni Galizia
- Biology Department, University of KonstanzKonstanzGermany
- Zukunftskolleg, University of KonstanzKonstanzGermany
| | - Nathaniel J Dominy
- Zukunftskolleg, University of KonstanzKonstanzGermany
- Department of Anthropology, Dartmouth CollegeHanoverUnited States
| | - Hannah M Rowland
- Max Planck Research Group Predators and Toxic Prey, Max Planck Institute for Chemical EcologyJenaGermany
| | - Mark E Hauber
- Department of Evolution, Ecology, and Behavior, School of Integrative Biology, University of Illinois at Urbana-ChampaignUrbana-ChampaignUnited States
| | - James P Higham
- Zukunftskolleg, University of KonstanzKonstanzGermany
- Department of Anthropology, New York UniversityNew YorkUnited States
| | - Ariana Strandburg-Peshkin
- Max Planck Institute of Animal BehaviorKonstanzGermany
- Centre for the Advanced Study of Collective Behaviour, University of KonstanzKonstanzGermany
- Biology Department, University of KonstanzKonstanzGermany
| | - Amanda D Melin
- Zukunftskolleg, University of KonstanzKonstanzGermany
- Department of Anthropology and Archaeology, University of CalgaryCalgaryCanada
- Alberta Children’s Hospital Research Institute, University of CalgaryCalgaryCanada
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13
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Phaniraj N, Wierucka K, Zürcher Y, Burkart JM. Who is calling? Optimizing source identification from marmoset vocalizations with hierarchical machine learning classifiers. J R Soc Interface 2023; 20:20230399. [PMID: 37848054 PMCID: PMC10581777 DOI: 10.1098/rsif.2023.0399] [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: 07/12/2023] [Accepted: 09/25/2023] [Indexed: 10/19/2023] Open
Abstract
With their highly social nature and complex vocal communication system, marmosets are important models for comparative studies of vocal communication and, eventually, language evolution. However, our knowledge about marmoset vocalizations predominantly originates from playback studies or vocal interactions between dyads, and there is a need to move towards studying group-level communication dynamics. Efficient source identification from marmoset vocalizations is essential for this challenge, and machine learning algorithms (MLAs) can aid it. Here we built a pipeline capable of plentiful feature extraction, meaningful feature selection, and supervised classification of vocalizations of up to 18 marmosets. We optimized the classifier by building a hierarchical MLA that first learned to determine the sex of the source, narrowed down the possible source individuals based on their sex and then determined the source identity. We were able to correctly identify the source individual with high precisions (87.21%-94.42%, depending on call type, and up to 97.79% after the removal of twins from the dataset). We also examine the robustness of identification across varying sample sizes. Our pipeline is a promising tool not only for source identification from marmoset vocalizations but also for analysing vocalizations of other species.
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Affiliation(s)
- Nikhil Phaniraj
- Institute of Evolutionary Anthropology (IEA), University of Zurich, Winterthurerstrasse 190, 8057 Zürich, Switzerland
- Neuroscience Center Zurich (ZNZ), University of Zurich and ETH Zurich, Winterthurerstrasse 190, 8057 Zürich, Switzerland
- Department of Biology, Indian Institute of Science Education and Research (IISER) Pune, Dr. Homi Bhabha Road, Pune 411008, India
| | - Kaja Wierucka
- Institute of Evolutionary Anthropology (IEA), University of Zurich, Winterthurerstrasse 190, 8057 Zürich, Switzerland
- Behavioral Ecology & Sociobiology Unit, German Primate Center, Leibniz Institute for Primate Research, Kellnerweg 4, 37077 Göttingen, Germany
| | - Yvonne Zürcher
- Institute of Evolutionary Anthropology (IEA), University of Zurich, Winterthurerstrasse 190, 8057 Zürich, Switzerland
| | - Judith M. Burkart
- Institute of Evolutionary Anthropology (IEA), University of Zurich, Winterthurerstrasse 190, 8057 Zürich, Switzerland
- Neuroscience Center Zurich (ZNZ), University of Zurich and ETH Zurich, Winterthurerstrasse 190, 8057 Zürich, Switzerland
- Center for the Interdisciplinary Study of Language Evolution (ISLE), University of Zurich, Affolternstrasse 56, 8050 Zürich, Switzerland
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14
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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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15
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Tyloo M. More is definitely different: The zebrafish as witness: Comment on "Structure and function in artificial, zebrafish and human neural networks" by Peng Ji et al. Phys Life Rev 2023; 46:71-72. [PMID: 37285666 DOI: 10.1016/j.plrev.2023.05.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Accepted: 05/24/2023] [Indexed: 06/09/2023]
Affiliation(s)
- Melvyn Tyloo
- Theoretical Division and Center for Nonlinear Studies (CNLS), Los Alamos National Laboratory, Los Alamos, 87545, NM, USA.
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16
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Pratissoli F, Reina A, Kaszubowski Lopes Y, Pinciroli C, Miyauchi G, Sabattini L, Groß R. Coherent movement of error-prone individuals through mechanical coupling. Nat Commun 2023; 14:4063. [PMID: 37463918 DOI: 10.1038/s41467-023-39660-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Accepted: 06/14/2023] [Indexed: 07/20/2023] Open
Abstract
We investigate how reliable movement can emerge in aggregates of highly error-prone individuals. The individuals-robotic modules-move stochastically using vibration motors. By coupling them via elastic links, soft-bodied aggregates can be created. We present distributed algorithms that enable the aggregates to move and deform reliably. The concept and algorithms are validated through formal analysis of the elastic couplings and experiments with aggregates comprising up to 49 physical modules-among the biggest soft-bodied aggregates to date made of autonomous modules. The experiments show that aggregates with elastic couplings can shrink and stretch their bodies, move with a precision that increases with the number of modules, and outperform aggregates with no, or rigid, couplings. Our findings demonstrate that mechanical couplings can play a vital role in reaching coherent motion among individuals with exceedingly limited and error-prone abilities, and may pave the way for low-power, stretchable robots for high-resolution monitoring and manipulation.
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Affiliation(s)
- Federico Pratissoli
- Department of Sciences and Methods for Engineering, University of Modena and Reggio Emilia, Reggio Emilia, Italy
- Department of Automatic Control and Systems Engineering, The University of Sheffield, Sheffield, UK
| | - Andreagiovanni Reina
- Department of Automatic Control and Systems Engineering, The University of Sheffield, Sheffield, UK
- IRIDIA, Université Libre de Bruxelles, Brussels, Belgium
| | | | - Carlo Pinciroli
- Department of Robotics Engineering, Worcester Polytechnic Institute, Worcester, MA, USA
| | - Genki Miyauchi
- Department of Automatic Control and Systems Engineering, The University of Sheffield, Sheffield, UK
| | - Lorenzo Sabattini
- Department of Sciences and Methods for Engineering, University of Modena and Reggio Emilia, Reggio Emilia, Italy
| | - Roderich Groß
- Department of Automatic Control and Systems Engineering, The University of Sheffield, Sheffield, UK.
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17
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Levin M. Darwin's agential materials: evolutionary implications of multiscale competency in developmental biology. Cell Mol Life Sci 2023; 80:142. [PMID: 37156924 PMCID: PMC10167196 DOI: 10.1007/s00018-023-04790-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2023] [Revised: 04/24/2023] [Accepted: 04/27/2023] [Indexed: 05/10/2023]
Abstract
A critical aspect of evolution is the layer of developmental physiology that operates between the genotype and the anatomical phenotype. While much work has addressed the evolution of developmental mechanisms and the evolvability of specific genetic architectures with emergent complexity, one aspect has not been sufficiently explored: the implications of morphogenetic problem-solving competencies for the evolutionary process itself. The cells that evolution works with are not passive components: rather, they have numerous capabilities for behavior because they derive from ancestral unicellular organisms with rich repertoires. In multicellular organisms, these capabilities must be tamed, and can be exploited, by the evolutionary process. Specifically, biological structures have a multiscale competency architecture where cells, tissues, and organs exhibit regulative plasticity-the ability to adjust to perturbations such as external injury or internal modifications and still accomplish specific adaptive tasks across metabolic, transcriptional, physiological, and anatomical problem spaces. Here, I review examples illustrating how physiological circuits guiding cellular collective behavior impart computational properties to the agential material that serves as substrate for the evolutionary process. I then explore the ways in which the collective intelligence of cells during morphogenesis affect evolution, providing a new perspective on the evolutionary search process. This key feature of the physiological software of life helps explain the remarkable speed and robustness of biological evolution, and sheds new light on the relationship between genomes and functional anatomical phenotypes.
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Affiliation(s)
- Michael Levin
- Allen Discovery Center at Tufts University, 200 Boston Ave. 334 Research East, Medford, MA, 02155, USA.
- Wyss Institute for Biologically Inspired Engineering at Harvard University, 3 Blackfan St., Boston, MA, 02115, USA.
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18
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Papadopoulou M, Fürtbauer I, O'Bryan LR, Garnier S, Georgopoulou DG, Bracken AM, Christensen C, King AJ. Dynamics of collective motion across time and species. Philos Trans R Soc Lond B Biol Sci 2023; 378:20220068. [PMID: 36802781 PMCID: PMC9939269 DOI: 10.1098/rstb.2022.0068] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Accepted: 11/17/2022] [Indexed: 02/21/2023] Open
Abstract
Most studies of collective animal behaviour rely on short-term observations, and comparisons of collective behaviour across different species and contexts are rare. We therefore have a limited understanding of intra- and interspecific variation in collective behaviour over time, which is crucial if we are to understand the ecological and evolutionary processes that shape collective behaviour. Here, we study the collective motion of four species: shoals of stickleback fish (Gasterosteus aculeatus), flocks of homing pigeons (Columba livia), a herd of goats (Capra aegagrus hircus) and a troop of chacma baboons (Papio ursinus). First, we describe how local patterns (inter-neighbour distances and positions), and group patterns (group shape, speed and polarization) during collective motion differ across each system. Based on these, we place data from each species within a 'swarm space', affording comparisons and generating predictions about the collective motion across species and contexts. We encourage researchers to add their own data to update the 'swarm space' for future comparative work. Second, we investigate intraspecific variation in collective motion over time and provide guidance for researchers on when observations made over different time scales can result in confident inferences regarding species collective motion. This article is part of a discussion meeting issue 'Collective behaviour through time'.
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Affiliation(s)
- Marina Papadopoulou
- Biosciences, School of Biosciences, Geography and Physics, Faculty of Science and Engineering, Swansea University, SA2 8PP Swansea, UK
| | - Ines Fürtbauer
- Biosciences, School of Biosciences, Geography and Physics, Faculty of Science and Engineering, Swansea University, SA2 8PP Swansea, UK
| | - Lisa R. O'Bryan
- Department of Psychological Sciences, Rice University, Houston, TX 77005, USA
| | - Simon Garnier
- Department of Biological Sciences, New Jersey Institute of Technology, Newark, NJ 07102, USA
| | - Dimitra G. Georgopoulou
- Biosciences, School of Biosciences, Geography and Physics, Faculty of Science and Engineering, Swansea University, SA2 8PP Swansea, UK
- Institute of Marine Biology, Biotechnology & Aquaculture, HCMR, 71500 Hersonissos, Crete, Greece
| | - Anna M. Bracken
- Biosciences, School of Biosciences, Geography and Physics, Faculty of Science and Engineering, Swansea University, SA2 8PP Swansea, UK
- School of Biodiversity, One Health and Veterinary Medicine, Graham Kerr Building, Glasgow G12 8QQ, UK
| | - Charlotte Christensen
- Biosciences, School of Biosciences, Geography and Physics, Faculty of Science and Engineering, Swansea University, SA2 8PP Swansea, UK
- Department of Evolutionary Biology and Environmental Studies, University of Zurich, 8057 Zürich, Switzerland
| | - Andrew J. King
- Biosciences, School of Biosciences, Geography and Physics, Faculty of Science and Engineering, Swansea University, SA2 8PP Swansea, UK
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19
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Mangan M, Floreano D, Yasui K, Trimmer BA, Gravish N, Hauert S, Webb B, Manoonpong P, Szczecinski N. A virtuous cycle between invertebrate and robotics research: perspective on a decade of Living Machines research. BIOINSPIRATION & BIOMIMETICS 2023; 18:035005. [PMID: 36881919 DOI: 10.1088/1748-3190/acc223] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Accepted: 03/07/2023] [Indexed: 06/18/2023]
Abstract
Many invertebrates are ideal model systems on which to base robot design principles due to their success in solving seemingly complex tasks across domains while possessing smaller nervous systems than vertebrates. Three areas are particularly relevant for robot designers: Research on flying and crawling invertebrates has inspired new materials and geometries from which robot bodies (their morphologies) can be constructed, enabling a new generation of softer, smaller, and lighter robots. Research on walking insects has informed the design of new systems for controlling robot bodies (their motion control) and adapting their motion to their environment without costly computational methods. And research combining wet and computational neuroscience with robotic validation methods has revealed the structure and function of core circuits in the insect brain responsible for the navigation and swarming capabilities (their mental faculties) displayed by foraging insects. The last decade has seen significant progress in the application of principles extracted from invertebrates, as well as the application of biomimetic robots to model and better understand how animals function. This Perspectives paper on the past 10 years of the Living Machines conference outlines some of the most exciting recent advances in each of these fields before outlining lessons gleaned and the outlook for the next decade of invertebrate robotic research.
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Affiliation(s)
- Michael Mangan
- The University of Sheffield, Mappin St, Sheffield S10 2TN, United Kingdom
| | - Dario Floreano
- Ecole Polytechnique Federale de Lausanne, Laboratory of Intelligent Systems, Station 9, Lausanne CH-1015, Switzerland
| | - Kotaro Yasui
- Tohoku University, Frontier Research Institute for Interdisciplinary Sciences, 6-3 Aramaki aza Aoba, Aoba-ku, Sendai 980-8578, Japan
| | - Barry A Trimmer
- Tufts University, Biology, 200 Boston Av, Boston, MA 02111, United States of America
| | - Nick Gravish
- University of California San Diego, Mechanical and Aerospace Engineering, Building EBU II, La Jolla, CA 92093, United States of America
| | - Sabine Hauert
- University of Bristol, Engineering Mathematics, Bristol BS8 1QU, United Kingdom
| | - Barbara Webb
- University of Edinburgh, School of Informatics, 10 Crichton St, Edinburgh EH8 9AB, United Kingdom
| | - Poramate Manoonpong
- College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, People's Republic of China
- Bio-Inspired Robotics and Neural Engineering Laboratory, School of Information Science and Technology, Vidyasirimedhi Institute of Science and Technology, Wangchan Valley, Rayong 21210, Thailand
| | - Nicholas Szczecinski
- West Virginia University, Mechanical and Aerospace Engineering, Morgantown, WV 26506-6201, United States of America
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20
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Ritter M, Pritz J, Morscheck L, Baumann E, Boos M. In no uncertain terms: Group cohesion did not affect exploration and group decision making under low uncertainty. Front Psychol 2023; 14:1038262. [PMID: 36760456 PMCID: PMC9905233 DOI: 10.3389/fpsyg.2023.1038262] [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: 09/06/2022] [Accepted: 01/09/2023] [Indexed: 01/26/2023] Open
Abstract
Group decision making under uncertainty often requires groups to balance exploration of their environment with exploitation of the seemingly best option. In order to succeed at this collective induction, groups need to merge the knowledge of all group members and combine goal-oriented and social motivations (i.e., group cohesion). This paper presents three studies that investigate whether more cohesive groups perform worse at collective induction tasks as they spend less time exploring possible options. Study 1 simulates group decision making with the ε-greedy algorithm in order to identify suitable manipulations of group cohesion and investigate how differing exploration lengths can affect outcomes of group decisions. Study 2 (N = 108, 18 groups á 6 participants) used an experimental manipulation of group cohesion in a simple card choice task to investigate how group cohesion might affect group decision making when only limited social information is available. Study 3 (N = 96, 16 groups á 6 participants) experimentally manipulated group cohesion and used the HoneyComb paradigm, a movement-based group experiment platform, to investigate which group processes would emerge during decision making and how these processes would affect the relationships between group cohesion, exploration length, and group decision making. Study 1 found that multiplicative cohesion rewards have detrimental effects on group decision making, while additive group rewards could ameliorate negative effects of the cohesion reward, especially when reported separately from task rewards. Additionally, exploration length was found to profoundly affect decision quality. Studies 2 and 3 showed that groups could identify the best reward option successfully, regardless of group cohesion manipulation. This effect is interpreted as a ceiling effect as the decision task was likely too easy to solve. Study 3 identified that spatial group cohesion on the playing field correlated with self-reported entitativity and leader-/followership emerged spontaneously in most groups and correlated with self-reported perceptions of leader-/followership in the game. We discuss advantages of simulation studies, possible adaptations to the ε-greedy algorithm, and methodological aspects of measuring behavioral group cohesion and leadership to inform empirical studies investigating group decision making under uncertainty.
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21
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Pérez-Campanero Antolín N, Taylor GK. Gap selection and steering during obstacle avoidance in pigeons. J Exp Biol 2023; 226:jeb244215. [PMID: 36576032 PMCID: PMC10086542 DOI: 10.1242/jeb.244215] [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: 03/02/2022] [Accepted: 12/07/2022] [Indexed: 12/29/2022]
Abstract
The ability of birds to fly through cluttered environments has inspired biologists interested in understanding its underlying mechanisms, and engineers interested in applying its underpinning principles. To analyse this problem empirically, we break it down into two distinct, but related, questions: How do birds select which gaps to aim for? And how do they steer through them? We answered these questions using a combined experimental and modelling approach, in which we released pigeons (Columbia livia domestica) inside a large hall with an open exit separated from the release point by a curtain creating two vertical gaps - one of which was obstructed by an obstacle. We tracked the birds using a high-speed motion capture system, and found that their gap choice seemed to be biased by their intrinsic handedness, rather than determined by extrinsic cues such as the size of the gap or its alignment with the destination. We modelled the pigeons' steering behaviour algorithmically by simulating their flight trajectories under a set of six candidate guidance laws, including those used previously to model target-oriented flight behaviours in birds. We found that their flights were best modelled by delayed proportional navigation commanding turning in proportion to the angular rate of the line-of-sight from the pigeon to the midpoint of the gap. Our results are consistent with this being a two-phase behaviour, in which the pigeon heads forward from the release point before steering towards the midpoint of whichever gap it chooses to aim for under closed-loop guidance. Our findings have implications for the sensorimotor mechanisms that underlie clutter negotiation in birds, uniting this with other kinds of target-oriented behaviours including aerial pursuit.
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Affiliation(s)
| | - Graham K. Taylor
- Department of Biology, University of Oxford, 11A Mansfield Road, Oxford OX1 3SZ, UK
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22
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Granger J, Johnsen S. Collective movement as a solution to noisy navigation and its vulnerability to population loss. Proc Biol Sci 2022; 289:20221910. [PMID: 36382526 PMCID: PMC9667355 DOI: 10.1098/rspb.2022.1910] [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: 08/02/2022] [Accepted: 10/25/2022] [Indexed: 12/02/2023] Open
Abstract
Many animals use the geomagnetic field to migrate long distances with high accuracy; however, research has shown that individual responses to magnetic cues can be highly variable. Thus, it has been hypothesized that magnetoreception alone is insufficient for accurate migrations and animals must either switch to a more accurate sensory cue or integrate their magnetic sense over time. Here we suggest that magnetoreceptive migrators could also use collective navigation strategies. Using agent-based models, we compare agents utilizing collective navigation to both the use of a secondary sensory system and time-integration. Our models demonstrate that collective navigation allows for 70% success rates for noisy navigators. To reach the same success rates, a secondary sensory system must provide perfect navigation for over 73% of the migratory route, and time integration must integrate over 50 time-steps, indicating that magnetoreceptive animals could benefit from using collective navigation. Finally, we explore the impact of population loss on animals relying on collective navigation. We show that as population density decreases, a greater proportion of individuals fail to reach their destination and that a 50% population reduction can result in up to a 37% decrease in the proportion of individuals completing their migration.
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Affiliation(s)
- Jesse Granger
- Department of Biology, Duke University, Durham, NC 27708, USA
| | - Sönke Johnsen
- Department of Biology, Duke University, Durham, NC 27708, USA
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23
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Artime O, De Domenico M. From the origin of life to pandemics: emergent phenomena in complex systems. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2022; 380:20200410. [PMID: 35599559 PMCID: PMC9125231 DOI: 10.1098/rsta.2020.0410] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Accepted: 02/08/2022] [Indexed: 05/31/2023]
Abstract
When a large number of similar entities interact among each other and with their environment at a low scale, unexpected outcomes at higher spatio-temporal scales might spontaneously arise. This non-trivial phenomenon, known as emergence, characterizes a broad range of distinct complex systems-from physical to biological and social-and is often related to collective behaviour. It is ubiquitous, from non-living entities such as oscillators that under specific conditions synchronize, to living ones, such as birds flocking or fish schooling. Despite the ample phenomenological evidence of the existence of systems' emergent properties, central theoretical questions to the study of emergence remain unanswered, such as the lack of a widely accepted, rigorous definition of the phenomenon or the identification of the essential physical conditions that favour emergence. We offer here a general overview of the phenomenon of emergence and sketch current and future challenges on the topic. Our short review also serves as an introduction to the theme issue Emergent phenomena in complex physical and socio-technical systems: from cells to societies, where we provide a synthesis of the contents tackled in the issue and outline how they relate to these challenges, spanning from current advances in our understanding on the origin of life to the large-scale propagation of infectious diseases. This article is part of the theme issue 'Emergent phenomena in complex physical and socio-technical systems: from cells to societies'.
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Affiliation(s)
- Oriol Artime
- Fondazione Bruno Kessler, Via Sommarive 18, Povo, TN 38123, Italy
| | - Manlio De Domenico
- Department of Physics and Astronomy ‘Galileo Galilei’, University of Padua, Padova, Veneto, Italy
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24
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Schad L, Fischer J. Opportunities and risks in the use of drones for studying animal behaviour. Methods Ecol Evol 2022. [DOI: 10.1111/2041-210x.13922] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Lukas Schad
- Cognitive Ethology Laboratory German Primate Center Göttingen Germany
- Leibniz ScienceCampus Primate Cognition Göttingen Germany
| | - Julia Fischer
- Cognitive Ethology Laboratory German Primate Center Göttingen Germany
- Leibniz ScienceCampus Primate Cognition Göttingen Germany
- Department for Primate Cognition Georg‐August‐University Göttingen Göttingen Germany
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25
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Farine DR. Collective behaviour: Jackdaws vote to leave with their voice. Curr Biol 2022; 32:R467-R469. [PMID: 35609544 DOI: 10.1016/j.cub.2022.03.070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Making a decision as a group requires not only choosing where to go but also when to go. A new study provides experimental evidence that, in jackdaws, vocalisations facilitate synchronous early morning departures from communal roosts.
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Affiliation(s)
- Damien R Farine
- Department of Evolutionary Biology and Environmental Studies, University of Zurich, Winterthurerstrasse 190, 8057 Zürich, Switzerland; Department of Collective Behavior, Max Planck Institute of Animal Behavior, Universitätsstraße 10, 78464 Konstanz, Germany; Division of Ecology and Evolution, Research School of Biology, Australian National University, 46 Sullivans Creek Road, Canberra, ACT 2600, Australia.
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26
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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.
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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:
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Papadopoulou M, Hildenbrandt H, Sankey DWE, Portugal SJ, Hemelrijk CK. Self-organization of collective escape in pigeon flocks. PLoS Comput Biol 2022; 18:e1009772. [PMID: 35007287 PMCID: PMC8782486 DOI: 10.1371/journal.pcbi.1009772] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Revised: 01/21/2022] [Accepted: 12/19/2021] [Indexed: 11/22/2022] Open
Abstract
Bird flocks under predation demonstrate complex patterns of collective escape. These patterns may emerge by self-organization from local interactions among group-members. Computational models have been shown to be valuable for identifying what behavioral rules may govern such interactions among individuals during collective motion. However, our knowledge of such rules for collective escape is limited by the lack of quantitative data on bird flocks under predation in the field. In the present study, we analyze the first GPS trajectories of pigeons in airborne flocks attacked by a robotic falcon in order to build a species-specific model of collective escape. We use our model to examine a recently identified distance-dependent pattern of collective behavior: the closer the prey is to the predator, the higher the frequency with which flock members turn away from it. We first extract from the empirical data of pigeon flocks the characteristics of their shape and internal structure (bearing angle and distance to nearest neighbors). Combining these with information on their coordination from the literature, we build an agent-based model adjusted to pigeons' collective escape. We show that the pattern of turning away from the predator with increased frequency when the predator is closer arises without prey prioritizing escape when the predator is near. Instead, it emerges through self-organization from a behavioral rule to avoid the predator independently of their distance to it. During this self-organization process, we show how flock members increase their consensus over which direction to escape and turn collectively as the predator gets closer. Our results suggest that coordination among flock members, combined with simple escape rules, reduces the cognitive costs of tracking the predator while flocking. Such escape rules that are independent of the distance to the predator can now be investigated in other species. Our study showcases the important role of computational models in the interpretation of empirical findings of collective behavior.
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Affiliation(s)
- Marina Papadopoulou
- Groningen Institute for Evolutionary Life Sciences, University of Groningen, Groningen, The Netherlands
| | - Hanno Hildenbrandt
- Groningen Institute for Evolutionary Life Sciences, University of Groningen, Groningen, The Netherlands
| | - Daniel W. E. Sankey
- Centre for Ecology and Conservation, University of Exeter, Penryn, United Kingdom
| | - Steven J. Portugal
- Department of Biological Sciences, School of Life and Environmental Sciences, Royal Holloway University of London, Egham, United Kingdom
| | - Charlotte K. Hemelrijk
- Groningen Institute for Evolutionary Life Sciences, University of Groningen, Groningen, The Netherlands
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