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Decoding Social Integration in Schooling Fish Using Closed-Loop Real-Virtual Interactions
PRX Life 4, 043007 – Published 29 October, 2026
DOI: https://doi.org/10.1103/18qw-sfyd
Abstract
Collective motion in animal groups emerges from local interactions, yet how individuals select and integrate social information remains poorly understood. In schooling fish, inferring which neighbors drive an individual's behavior at any given moment is extremely challenging because the analysis of trajectories alone does not unambiguously reveal the underlying causal interactions that generate the observed motion. Here, we used a closed-loop virtual-reality system in which a single real rummy-nose tetra Hemigrammus rhodostomus interacted in real time with four model-driven virtual conspecifics whose interaction rules could be precisely controlled. By systematically varying the number of influential neighbors of the virtual fish and combining experiments with simulations, we quantified how social information filtering shapes individual and collective dynamics. Collective coordination increased with stronger social coupling, but analyses of kinematics, spatial organization, correlations, and turning dynamics consistently showed that the real fish behaved as if it were responding primarily to the single most influential neighbor, whose identity could change over time. These results demonstrate that selective interaction with the most influential neighbor is sufficient to sustain coordinated group motion and highlight the power of biohybrid closed-loop experiments to reveal causal mechanisms of collective behavior.
Physics Subject Headings (PhySH)
Research News
Schooling Fish May Have Just One Influencer
By studying a real fish swimming among virtual fish, researchers uncover the rules that a fish follows when schooling.
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References (58)
- S. Camazine, J.-L. Deneubourg, N. Franks, J. Sneyd, G. Theraulaz, and E. Bonabeau, Self-Organization in Biological Systems (Princeton University Press, Princeton, NJ, 2001).
- T. Vicsek and A. Zafeiris, Collective motion, Phys. Rep. 517, 71 (2012).
- D. J. T. Sumpter, Collective Animal Behavior (Princeton University Press, Princeton, NJ, 2010).
- D. S. Pavlov and A. O. Kasumyan, Patterns and Mechanisms of Schooling Behavior in Fish: A Review, J.Ichthyology 40, Suppl. 2, S163 (2000).
- J. E. Herbert-Read, A. Perna, R. P. Mann, T. M. Schaerf, D. J. T. Sumpter, and A. J. W. Ward, Inferring the rules of interaction of shoaling fish, Proc. Natl. Acad. Sci. USA 108, 18726 (2011).
- Y. Katz, K. Tunstrøm, C. C. Ioannou, C. Huepe, and I. D. Couzin, Inferring the structure and dynamics of interactions in schooling fish, Proc. Natl. Acad. Sci. USA 108, 18720 (2011).
- J. Gautrais, F. Ginelli, R. Fournier, S. Blanco, M. Soria, H. Chaté, and G. Theraulaz, Deciphering interactions in moving animal groups, PLoS Comput. Biol. 8, e1002678 (2012).
- U. Lopez, J. Gautrais, I. D. Couzin, and G. Theraulaz, From behavioural analyses to models of collective motion in fish schools, Interface Focus 2, 693 (2012).
- D. S. Calovi, A. Litchinko, V. Lecheval, U. Lopez, A. Pérez Escudero, H. Chaté, C. Sire, and G. Theraulaz, Disentangling and modeling interactions in fish with burst-and-coast swimming reveal distinct alignment and attraction behaviors, PLoS Comput. Biol. 14, e1005933 (2018).
- J. Toner and Y. Tu, Flocks, herds and schools: A quantitative theory of flocking, Phys. Rev. E 58, 4828 (1998).
- I. Giardina, Collective behavior in animal groups: Theoretical models and empirical studies, HFSP J. 2, 205 (2008).
- S. Ramaswamy, The mechanics and statistics of active matter, Annu. Rev. Condens. Matter Phys. 1, 323 (2010).
- S. V. Viscido, J. K. Parrish, and D. Grünbaum, The effect of population size and number of influential neighbors on the emergent properties of fish schools, Ecol. Modell. 183, 347 (2005).
- B. H. Lemasson, J. J. Anderson, and R. A. Goodwin, Collective motion in animal groups from a neurobiological perspective: The adaptive benefits of dynamic sensory loads and selective attention, J. Theor. Biol. 261, 501 (2009).
- N. W. F. Bode, D. W. Franks, and A. J. Wood, Limited interactions in flocks: Relating model simulations to empirical data, J. R. Soc. Interface 8, 301 (2011).
- A. M. Calvão and E. Brigatti, The role of neighbours selection on cohesion and order of swarms, PLoS ONE 9, e94221 (2014).
- Z. Zheng, X. Lei, and X. Peng, Selective interaction and its effect on collective motion, Sci. Rep. 12, 8601 (2022).
- B. H. Lemasson, J. J. Anderson, and R. A. Goodwin, Motion-guided attention promotes adaptive communications during social navigation, Proc. R. Soc. B 280, 20122003 (2013).
- L. Jiang, L. Giuggioli, A. Perna, R. Escobedo, V. Lecheval, C. Sire, Z. Han, and G. Theraulaz, Identifying influential neighbors in animal flocking, PLoS Comput. Biol. 13, e1005822 (2017).
- L. Lei, R. Escobedo, C. Sire, and G. Theraulaz, Computational and robotic modeling reveal parsimonious combinations of interactions between individuals in schooling fish, PLoS Comput. Biol. 16, e1007194 (2020).
- S. Ito and N. Uchida, Selective decision-making and collective behavior of fish by the motion of visual attention, Proc. Natl. Acad. Sci. Nexus 3, pgae264 (2024).
- S. Weitz, S. Blanco, R. Fournier, J. Gautrais, C. Jost, and G. Theraulaz, Modeling collective animal behavior with a cognitive perspective: A methodological framework, PLoS ONE 7, e38588 (2012).
- A. Cavagna, D. Conti, I. Giardina, T. S. Grigera, S. Melillo, and M. Viale, Spatio-temporal correlations in models of collective motion ruled by different dynamical laws, Phys. Biol. 13, 065001 (2016).
- A. Jouary, J.-F. Perrier, A. Legendre, and K. Doya, A 2d virtual reality system for visual goal-driven navigation in zebrafish larvae, Sci. Rep. 6, 34015 (2016).
- H. Naik, R. Bastien, N. Navab, and I. D. Couzin, Animals in virtual environments, IEEE Trans. Visualiz. Comput. Graph. 26, 2073 (2020).
- K.-H. Huang, P. Rupprecht, T. Frank, K. Kawakami, T. Bouwmeester, and R. W. Friedrich, A virtual reality system to analyze neural activity and behavior in adult zebrafish, Nat. Methods 17, 343 (2020).
- S. Sanchez, R. Escobedo, R. Bastien, B. Lenseigne, A. Denis, M. Moreau, M. Combe, A. D. Straw, C. Sire, and G. Theraulaz, An open-source closed-loop virtual reality system to investigate social interactions and collective behavior in fish, PLoS ONE 21, e0339909 (2026).
- P. De Lellis, E. Cadolini, A. Croce, Y. Yang, M. Di Bernardo, and M. Porfiri, Model-based feedback control of live zebrafish behavior via interaction with a robotic replica, IEEE Trans. Rob. 36, 28 (2020).
- J. Halloy, G. Sempo, G. Caprari, C. Rivault, M. Asadpour, F. Tâche, I. Saïd, V. Durier, S. Canonge, J. M. Amé, et al., Social integration of robots into groups of cockroaches to control self-organized choices, Science 318, 1155 (2007).
- G. Amichay, L. Li, M. Nagy, and I. D. Couzin, Revealing the mechanism and function underlying pairwise temporal coupling in collective motion, Nat. Commun. 15, 4356 (2024).
- R. Escobedo, J. Reynaud, R. Bastien, S. Sanchez, C. Sire, and G. Theraulaz, Closed-loop real-virtual interactions validate 3d model of social coordination in fish, bioRxiv 10.64898/2025.12.07.692829 (2025).
- F. Bonnet, A. Gribovskiy, J. Halloy, and F. Mondada, Closed-loop interactions between a shoal of zebrafish and a group of robotic fish in a circular corridor, Swarm Intell. 12, 227 (2018).
- A. Filella, F. Nadal, C. Sire, E. Kanso, and C. Eloy, Model of collective fish behavior with hydrodynamic interactions, Phys. Rev. Lett. 120, 198101 (2018).
- M. Porfiri, P. Zhang, and S. D. Peterson, Hydrodynamic model of fish orientation in a channel flow, eLife 11, e75225 (2022).
- R. Das, S. D. Peterson, and M. Porfiri, Disentangling coexisting sensory pathways of interaction in schooling fish, Flow 5, E16 (2025).
- A. McKee, A. P. Soto, P. Chen, and M. J. McHenry, The sensory basis of schooling by intermittent swimming in the rummy-nose tetra (Hemigrammus rhodostomus), Proc. R. Soc. B 287, 20200568 (2020).
- V. Lecheval, L. Jiang, P. Tichit, C. Sire, C. Hemelrijk, and G. Theraulaz, Social conformity and propagation of information in collective u-turns of fish schools, Proc. R. Soc. B 285, 20180251 (2018).
- B. Lafoux, J. Moscatelli, R. Godoy-Diana, and B. Thiria, Illuminance-tuned collective motion in fish, Commun. Biol. 6, 585 (2023).
- E. Bonabeau, M. Dorigo, and G. Theraulaz, Swarm Intelligence: From Natural to Artificial Systems (Oxford University Press, Oxford, 1999).
- See Supplemental Material at http://link.aps.org/supplemental/10.1103/18qw-sfyd for the suplementary text, eleven supplementary figures, five suplementary tables, and seven supplementary movies.
- C. K. Hemelrijk and H. Hildenbrandt, Scale-free correlations, influential neighbours and speed control in flocks of birds, J. Stat. Phys. 158, 563 (2015).
- S. B. Rosenthal, C. R. Twomey, A. T. Hartnett, H. S. Wu, and I. D. Couzin, Revealing the hidden networks of interaction in mobile animal groups allows prediction of complex behavioral contagion, Proc. Natl. Acad. Sci. USA 112, 4690 (2015).
- L. Li, M. Nagy, G. Amichay, R. Wu, W. Wang, O. Deussen, and I. D. Couzin, Reverse engineering the control law for schooling in zebrafish using virtual reality, Sci. Rob. 10, eadq6784 (2025).
- N. W. F. Bode, A. J. Wood, and D. W. Franks, Social networks and models for collective motion in animals, Behav. Ecol. Sociobiol. 65, 117 (2011).
- J. R. Stowers, M. Hofbauer, R. Bastien, J. Griessner, P. Higgins, S. Farooqui, R. M. Fischer, K. Nowikovsky, W. Haubensak, I. D. Couzin, K. Tessmar-Raible, and A. D. Straw, Virtual reality for freely moving animals, Nat. Methods 14, 995 (2017).
- T. Xue, X. Li, G. Lin, R. Escobedo, Z. Han, X. Chen, C. Sire, and G. Theraulaz, Tuning social interactions' strength drives collective response to light intensity in schooling fish, PLoS Comput. Biol. 19, e1011636 (2023).
- H. E. A. MacGregor, J. E. Herbert-Read, and C. C. Ioannou, Information can explain the dynamics of group order in animal collective behaviour, Nat. Commun. 11, 2737 (2020).
- N. T. Ouellette, A physics perspective on collective animal behavior, Phys. Biol. 19, 021004 (2022).
- K. Krishnan, A. Muthukumar, S. Sterrett, P. Pflitsch, A. L. Fairhall, M. Fishman, A. Bahl, H. Zwaka, and F. Engert, Attentional switching in larval zebrafish, Sci. Adv. 11, eads4994 (2025).
- V. Jadhav, V. Guttal, and D. R. Masila, Randomness in the choice of neighbours promotes cohesion in mobile animal groups, R. Soc. Open Sci. 9, 220124 (2022).
- I. Aoki, A simulation study on the schooling mechanism in fish, Bull. Jpn. Soc. Sci. Fish. 48, 1081 (1982).
- A. Huth and C. Wissel, The simulation of the movement of fish schools, J. Theor. Biol. 156, 365 (1992).
- I. D. Couzin, J. Krause, R. James, G. D. Ruxton, and N. R. Franks, Collective memory and spatial sorting in animal groups, J. Theor. Biol. 218, 1 (2002).
- T. Vicsek, A. Czirók, E. Ben-Jacob, I. Cohen, and O. Shochet, Novel type of phase transition in a system of self-driven particles, Phys. Rev. Lett. 75, 1226 (1995).
- A. Basu, I. R. Harris, and S. Basu, Minimum distance estimation: The approach using density based distances, in Handbook of Statistics: Robust Inference, edited by G. S. Maddala and C. R. Rao (Elsevier Science, New York, 1997), Vol. 15, pp. 21–48.
- R. Beran, Minimum Hellinger distance estimates for parametric models, Ann. Statist. 5, 445 (1977).
- Data: https://doi.org/10.6084/m9.figshare.31378621.
- Code: https://doi.org/10.6084/m9.figshare.31386349.