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Informative and misinformative interactions in a school of fish

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Abstract

Quantifying distributed information processing is crucial to understanding collective motion in animal groups. Recent studies have begun to apply rigorous methods based on information theory to quantify such distributed computation. Following this perspective, we use transfer entropy to quantify dynamic information flows locally in space and time across a school of fish during directional changes around a circular tank, i.e., U-turns. This analysis reveals peaks in information flows during collective U-turns and identifies two different flows: an informative flow (positive transfer entropy) from fish that have already turned to fish that are turning, and a misinformative flow (negative transfer entropy) from fish that have not turned yet to fish that are turning. We also reveal that the information flows are related to relative position and alignment between fish and identify spatial patterns of information and misinformation cascades. This study offers several methodological contributions and we expect further application of these methodologies to reveal intricacies of self-organisation in other animal groups and active matter in general.

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Notes

  1. With that said, we have also shown that techniques employed in this study are also successful in identifying information flows in groups with smoother motion dynamics (Wang et al. 2012; Miller et al. 2014).

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Acknowledgements

GT designed research; VL, PT and GT performed research; VL, LJ, PT, RW and GT analysed data. EC, JL, RW and MP developed information dynamics methods, performed information-theoretic analysis, and identified information flows and motifs. EC designed, developed and run software for the information-theoretic analysis. GT, JL, EC and MP conceived and analysed information cascade. EC, JL and MP wrote the paper. GT and VL edited the manuscript and contributed to the writing.

Funding E.C. was supported by the University of Sydney’s “Postgraduate Scholarship in the field of Complex Systems” from Faculty of Engineering & IT and by a CSIRO top-up scholarship. L.J. was supported by a grant from the China Scholarship Council (CSC NO.201506040167). V.L. was supported by a doctoral fellowship from the scientific council of the University Paul Sabatier. This study was supported by grants from the Centre National de la Recherche Scientifique and University Paul Sabatier (project Dynabanc). J.L. was supported through the Australian Research Council DECRA grant DE160100630. M.P. was supported by The University of Sydney’s DVC Research Strategic Research Excellence Initiative (SREI-2020) project, “CRISIS: Crisis Response in Interdependent Social-Infrastructure Systems” (IRMA 194163). Sydney Informatics Hub at the University of Sydney provided access to HPC computational resources that have contributed to the research results reported within the paper.

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Authors and Affiliations

  1. Complex Systems Research Group and Centre for Complex Systems, Faculty of Engineering & IT, The University of Sydney, Sydney, NSW, 2006, Australia

    Emanuele Crosato, Joseph T. Lizier & Mikhail Prokopenko

  2. School of Systems Science, Beijing Normal University, Beijing, 100875, People’s Republic of China

    Li Jiang

  3. Centre de Recherches sur la Cognition Animale, Centre de Biologie Intégrative (CBI), Centre National de la Recherche Scientifique (CNRS), Université Paul Sabatier (UPS), 31062, Toulouse Cedex 9, France

    Li Jiang, Valentin Lecheval, Pierre Tichit & Guy Theraulaz

  4. Groningen Institute for Evolutionary Life Sciences, Centre for Life Sciences, University of Groningen, Nijenborgh 7, 9747AG, Groningen, The Netherlands

    Valentin Lecheval

  5. CSIRO Data61, PO Box 76, Epping, NSW, 1710, Australia

    Emanuele Crosato & X. Rosalind Wang

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  1. Emanuele Crosato
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  2. Li Jiang
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  3. Valentin Lecheval
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  4. Joseph T. Lizier
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  8. Mikhail Prokopenko
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Corresponding author

Correspondence to Emanuele Crosato.

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The authors declare no conflict of interest.

Ethical standard

All experiments have been approved by the Ethics Committee for Animal Experimentation of the Toulouse Research Federation in Biology N1 and comply with the European legislation for animal welfare.

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Crosato, E., Jiang, L., Lecheval, V. et al. Informative and misinformative interactions in a school of fish. Swarm Intell 12, 283–305 (2018). https://doi.org/10.1007/s11721-018-0157-x

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