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      The geometry of decision-making in individuals and collectives

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          Significance

          Almost all animals must make decisions on the move. Here, employing an approach that integrates theory and high-throughput experiments (using state-of-the-art virtual reality), we reveal that there exist fundamental geometrical principles that result from the inherent interplay between movement and organisms’ internal representation of space. Specifically, we find that animals spontaneously reduce the world into a series of sequential binary decisions, a response that facilitates effective decision-making and is robust both to the number of options available and to context, such as whether options are static (e.g., refuges) or mobile (e.g., other animals). We present evidence that these same principles, hitherto overlooked, apply across scales of biological organization, from individual to collective decision-making.

          Abstract

          Choosing among spatially distributed options is a central challenge for animals, from deciding among alternative potential food sources or refuges to choosing with whom to associate. Using an integrated theoretical and experimental approach (employing immersive virtual reality), we consider the interplay between movement and vectorial integration during decision-making regarding two, or more, options in space. In computational models of this process, we reveal the occurrence of spontaneous and abrupt “critical” transitions (associated with specific geometrical relationships) whereby organisms spontaneously switch from averaging vectorial information among, to suddenly excluding one among, the remaining options. This bifurcation process repeats until only one option—the one ultimately selected—remains. Thus, we predict that the brain repeatedly breaks multichoice decisions into a series of binary decisions in space–time. Experiments with fruit flies, desert locusts, and larval zebrafish reveal that they exhibit these same bifurcations, demonstrating that across taxa and ecological contexts, there exist fundamental geometric principles that are essential to explain how, and why, animals move the way they do.

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          Most cited references66

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          Neural networks and physical systems with emergent collective computational abilities.

          J Hopfield (1982)
          Computational properties of use of biological organisms or to the construction of computers can emerge as collective properties of systems having a large number of simple equivalent components (or neurons). The physical meaning of content-addressable memory is described by an appropriate phase space flow of the state of a system. A model of such a system is given, based on aspects of neurobiology but readily adapted to integrated circuits. The collective properties of this model produce a content-addressable memory which correctly yields an entire memory from any subpart of sufficient size. The algorithm for the time evolution of the state of the system is based on asynchronous parallel processing. Additional emergent collective properties include some capacity for generalization, familiarity recognition, categorization, error correction, and time sequence retention. The collective properties are only weakly sensitive to details of the modeling or the failure of individual devices.
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            Novel Type of Phase Transition in a System of Self-Driven Particles

              Bookmark
              • Record: found
              • Abstract: not found
              • Article: not found

              Dynamics of pattern formation in lateral-inhibition type neural fields.

              S Amari (1977)
                Bookmark

                Author and article information

                Journal
                Proc Natl Acad Sci U S A
                Proc Natl Acad Sci U S A
                pnas
                PNAS
                Proceedings of the National Academy of Sciences of the United States of America
                National Academy of Sciences
                0027-8424
                1091-6490
                8 December 2021
                14 December 2021
                8 December 2021
                : 118
                : 50
                : e2102157118
                Affiliations
                [1] aDepartment of Collective Behaviour, Max Planck Institute of Animal Behavior , 78464 Konstanz, Germany;
                [2] bCentre for the Advanced Study of Collective Behaviour, University of Konstanz , 78464 Konstanz, Germany;
                [3] cDepartment of Biology, University of Konstanz , 78464 Konstanz, Germany;
                [4] dMTA-ELTE “Lendület” Collective Behaviour Research Group, Eötvös Loránd Research Network , 1117 Budapest, Hungary;
                [5] eDepartment of Biological Physics, Eötvös Loránd University , 1117 Budapest, Hungary;
                [6] fMTA-ELTE Statistical and Biological Physics Research Group, Eötvös Loránd Research Network , 1117 Budapest, Hungary;
                [7] gDepartment of Chemistry, University of Konstanz , 78464 Konstanz, Germany;
                [8] hDepartment of Physics and Astronomy, University of Waterloo , Waterloo, ON N2L 3G1, Canada;
                [9] iDepartment of Chemical and Biological Physics, Weizmann Institute of Science , Rehovot 76100, Israel
                Author notes
                1To whom correspondence may be addressed. Email: vivekhsridhar@ 123456gmail.com , lli@ 123456ab.mpg.de , or icouzin@ 123456ab.mpg.de .

                Edited by Raghavendra Gadagkar, Centre for Ecological Sciences, Indian Institute of Science, Bangalore, India; received February 2, 2021; accepted October 19, 2021

                Author contributions: V.H.S., N.S.G., and I.D.C. designed research; V.H.S., L.L., and B.R.S. performed research; V.H.S., L.L., M.N., and I.D.C. analyzed data; V.H.S., D.G., T.S., N.S.G., and I.D.C. constructed the model; and V.H.S., L.L., D.G., M.N., B.R.S., T.S., N.S.G., and I.D.C. wrote the paper.

                Author information
                http://orcid.org/0000-0001-6658-2232
                http://orcid.org/0000-0002-2447-6295
                http://orcid.org/0000-0003-0314-5259
                http://orcid.org/0000-0002-9712-1575
                http://orcid.org/0000-0001-7774-1139
                http://orcid.org/0000-0001-8556-4558
                Article
                202102157
                10.1073/pnas.2102157118
                8685676
                34880130
                c060c56b-2d24-4e55-a175-d707f31b32f6
                Copyright © 2021 the Author(s). Published by PNAS.

                This open access article is distributed under Creative Commons Attribution License 4.0 (CC BY).

                History
                : 19 October 2021
                Page count
                Pages: 8
                Funding
                Funded by: National Science Foundation (NSF) 100000001
                Award ID: IOS-1355061
                Award Recipient : Iain D Couzin
                Funded by: DOD | United States Navy | Office of Naval Research (ONR) 100000006
                Award ID: N00014-19-1-2556
                Award Recipient : Iain D Couzin
                Funded by: Baden-Württemberg Stiftung (Baden-Württemberg Foundation) 100008316
                Award ID: Struktur- und Innovationsfonds
                Award Recipient : Iain D Couzin
                Funded by: Deutsche Forschungsgemeinschaft (DFG) 501100001659
                Award ID: EXC 2117-422037984
                Award Recipient : Iain D Couzin
                Funded by: Max-Planck-Gesellschaft (MPG) 501100004189
                Award ID: Max Planck Institute of Animal Behavior
                Award Recipient : Iain D Couzin
                Funded by: Minerva Foundation (Minerva Stiftung) 501100001658
                Award ID: 712601
                Award Recipient : Nir S Gov
                Categories
                414
                408
                Biological Sciences
                Ecology
                Physical Sciences
                Biophysics and Computational Biology

                ring attractor,movement ecology,navigation,collective behavior,embodied choice

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