Biology subjects

Sire, C.

Publications and source records attributed to Sire, C..

18 recordsLinked to original sources

bioRxiv2026

Closed-loop robotic interactions reveal dynamic social filtering in schooling fish

Collective motion emerges from local interactions among individuals, yet whether interaction rules inferred from trajectory data correspond to the mechanisms actually used by animals remains unresolved. Here, we address this question using an autonomous closed-loop robotic fish implementing a data-driven model of social interactions reconstructed from the schooling fish Hemigrammus rhodostomus. The robot continuously updated its behavior from real-time tracking of freely swimming fish while reproducing spontaneous locomotion, wall avoidance, and anisotropic attraction and alignment. We compared entirely biological groups, biohybrid groups containing one robotic fish, and numerical simulations using identical behavioral descriptors across isolated individuals, pairs, and groups of five fish. The robotic fish successfully integrated into natural schools and reproduced the principal signatures of collective coordination, providing the first direct causal validation of interaction rules reconstructed from behavioral trajectories. Biohybrid experiments showed that a robot responding only to its single most influential neighbor was sufficient to sustain natural collective coordination. By contrast, numerical simulations reproduced the behavior of biological groups most accurately when each fish interacted with its two most influential neighbors. This discrepancy identifies the contribution of hydrodynamic interactions, which remain available to living fish but are absent from the robotic controller, demonstrating that physical and behavioral interactions jointly shape collective organization. These findings establish closed-loop biohybrid robotics as a powerful framework for experimentally testing the mechanisms underlying collective animal behavior. SignificanceInferring the behavioral mechanisms underlying collective animal behavior from trajectory data alone cannot establish causality. We combined a data-driven model of fish social interactions with an autonomous closed-loop robotic fish that continuously interacted with freely swimming conspecifics. This biohybrid approach provides the first direct causal validation of interaction rules reconstructed from behavioral trajectories. Comparing biological groups, biohybrid groups, and numerical simulations further reveals that hydrodynamic interactions complement social interactions in shaping collective organization. While living fish require information from their two most influential neighbors to reproduce natural schools, a robotic fish lacking hydrodynamic feedback achieves comparable coordination by responding to only its single most influential neighbor, demonstrating the power of closed-loop biohybrid robotics for testing mechanisms of collective behavior.

animal behavior and cognition↗

bioRxiv2026

Tracing information propagation in fish schools with a conditioned escape response

Collective escape responses are widespread in animal groups, yet how threat-related information spreads from a few informed individuals to the collective remains poorly understood because the identities of the individuals who initially detect danger are rarely experimentally controlled. Here, we combined aversive conditioning, behavioural tracking, and computational modelling to investigate how escape information propagates through schools of Puntigrus tetrazona. We conditioned selected fish to associate a green light with an aversive stimulus. Subsequently, we tested the escape response of schools containing one informed and four naive fish to the green light. One informed fish was sufficient to trigger a collective escape response. Upon stimulus onset, conditioned fish accelerated and crossed the hurdle, after which naive fish sequentially increased their speed and followed. Temporal correlations in speed revealed a hierarchical leader-follower structure mostly aligned with hurdle-crossing order. Neither initial distance, viewing angle, nor relative orientation to informed fish predicted the order of the escape sequence. An agent-based model incorporating local alignment and distance regulation reproduced experimental observations and suggested that the informed individual likely remains partly attentive to its neighbours during escape. Overall, our work provides a general framework for experimentally dissecting information transfer in animal groups and identifying the behavioural mechanisms underlying collective escape dynamics.

animal behavior and cognition↗

bioRxiv2026

Decoding social integration in schooling fish using closed-loop real-virtual interactions

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 individuals 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 were 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 bio-hybrid closed-loop experiments to reveal causal mechanisms of collective behavior.

animal behavior and cognition↗

bioRxiv2026

Swimming speed of schooling fish controls social interaction strength in open-loop immersive virtual reality

Collective motion in fish schools emerges from local attraction, alignment, and repulsion rules whose strengths vary with behavioural and biomechanical context. Although swimming speed is known to shape turning capacity, spatial positioning, and information flow, its causal influence on social interaction strength remains poorly quantified. Using an open-loop immersive virtual-reality system, we expose freely swimming Hemigrammus rhodostomus to a virtual conspecific moving along circular trajectories at six controlled speeds. This approach isolates the effect of neighbour speed on attraction, alignment, vertical positioning, and wall avoidance. Across all speeds, real fish reliably track the virtual fish and adjust their own speed toward that of the partner. Behavioural distributions reveal systematic changes in spatial positioning and perception angles, with fish shifting from leader-like to follower-like configurations as virtual-fish speed increases. A data-driven 3D self-propelled particle model, in which only the amplitudes of interaction functions are modulated, quantitatively reproduces these patterns. Model-based reconstructions shows that higher partner speed weakens both horizontal and vertical attraction, strengthens alignment, and increases wall repulsion. These results demonstrate that fish tune their social-interaction rules in a speed-dependent manner, revealing swimming speed as a key control parameter shaping coordination in minimal groups and, potentially, in larger schools.

animal behavior and cognition↗

bioRxiv2025

Closed-loop real-virtual interactions validate 3D model of social coordination in fish

Collective motion in animal groups arises from social interactions rules, yet uncovering these rules requires quantitative models grounded in real behavior. We developed a fully three-dimensional, data-driven model of pairwise interactions in the schooling fish Hemigrammus rhodostomus, reconstructing attraction, alignment from experiments with two real fish swimming freely in a hemispherical bowl. Simulations of this model quantitatively reproduced empirical distributions of speed, distance, and orientation. We then embedded the model into a closed-loop virtual reality system, allowing a real fish to interact in real time with a virtual conspecific whose movements were governed by the same interaction rules. This biohybrid setup revealed that the model captures key social interactions underlying coordinated swimming. Our results establish a robust validation framework linking data, models, and behavior, paving the way for hybrid biological-digital collectives.

animal behavior and cognition↗

bioRxiv2025

Group size and social interactions' strength determine collective response to perturbation in a model of burst-and-coast swimming fish

Collective responses to localized perturbations are essential for the adaptability of animal groups. Using a biologically grounded computational model of burst-and-coast swimming in Hemigrammus rhodostomus, we investigate how group size and the strength of social interactions shape collective dynamics under perturbation. The model integrates experimentally derived attraction and alignment rules, behavioral heterogeneity, and boundary effects within a circular tank. We identify four collective states (schooling, milling, turning, and swarming) and characterize a critical regime in which groups exhibit multistable dynamics. At this critical point, small subsets of perturbing individuals, defined by altered social interaction strengths, can induce sharp transitions to new collective states. In particular, such transitions occur in large groups (N = 100) but not in smaller ones (N = 25 or 50), highlighting a size-dependent sensitivity to disturbance. We show that both the nature of the perturbing individuals and the initial state of the group modulate the systems responsiveness. Our findings suggest that large groups may exploit criticality to remain both robust, flexible, and that individual variability can serve as a catalyst for adaptive reconfiguration. This work provides new insights into how the internal group structure and perturbation design influence collective behavior in animal groups.

animal behavior and cognition↗

bioRxiv2025

A low-cost immersive Virtual Reality system to study social interactions and collective behavior in fish

1This study introduces a low-cost, open-source, immersive Virtual Reality (VR) system designed to investigate real-time social interactions and collective behaviors in fish. Understanding collective animal behaviors, such as schooling in fish, presents significant observational challenges due to rapid and complex interactions. To address these difficulties, we developed an innovative closed-loop VR environment allowing precise control and measurement of interactions between real and virtual fish. This setup incorporates high-speed 3D tracking, real-time visual feedback, and automated data processing, creating realistic and interactive scenarios. We present experimental results, obtained with rummy-nose tetras (Hemigrammus rhodostomus ), showing that freely moving real fish consistently adjusted their speed, depth, and spatial positioning to follow virtual fish effectively. Fish matched moderate virtual speeds comfortably, struggled slightly at higher speeds, and actively maintained vertical positioning to sustain group cohesion. The implementation of this VR system provides critical insights into sensory and cognitive processes underlying collective movement and decision-making in fish. By revealing both flexibility and constraints in fish behavior, our findings underscore the potential of VR technology to study social interactions in controlled yet realistic settings. Ultimately, this VR system advances our understanding of collective animal behavior, providing a foundation for cross-species studies linking neural processes, behavioral interactions, and group dynamics.

animal behavior and cognition↗

bioRxiv2025

Experimental evidence of stress-induced critical state in schooling fish

How do animal groups dynamically adjust their collective behavior in response to environmental changes is an open and challenging question. Here, we investigate the mechanisms that allow fish schools to tune their collective state under stress, testing the hypothesis that these systems operate near criticality, a state maximizing sensitivity, responsiveness, and adaptability. We combine experiments and data-driven computational modeling to study how group size and stress influence the collective behavior of rummy-nose tetras (Hemigrammus rhodostomus). We quantify the collective state of fish schools using polarization, milling, and cohesion metrics and use a burst-and-coast model to infer the social interaction parameters that drive these behaviors. Our results indicate that group size modulates stress levels, with smaller groups experiencing higher baseline stress, likely due to a reduced social buffering effect. Under stress, fish adjust the strength of their social interactions in a way that leads the group into a critical state, thus enhancing its sensitivity to perturbations and facilitating rapid adaptation. However, large groups require an external stressor to enter the critical regime, whereas small groups are already near this state. Unlike previous studies suggesting that fish adjust their interaction network structure under risk, our results suggest that the intensity of social interactions, rather than network structure, governs collective state transitions. This simpler mechanism reduces cognitive demands while enabling dynamic adaptation. By revealing how stress and group size drive self-organization toward criticality, our study provides fundamental insights into the adaptability of collective biological systems and the emergent properties in animal groups.

animal behavior and cognition↗

bioRxiv2024

Collective phases and long-term dynamics in a fish school model with burst-and-coast swimming

Intermittent and asynchronous burst-and-coast swimming is widely adopted by various species of fish as an energy-efficient locomotion mode. This swimming mode significantly influences the way in which fish integrate information and make decisions in a social context. Here, we introduce a simplified fish school model in which individuals have an asynchronous burst-and-coast swimming mode and selectively interact only with one or two neighbors that exert the largest influence on their behavior and over a limited spatial range. The interactions consist for a fish to be attracted and aligned with these neighbors. We show that, by adjusting the interactions between individuals above a sufficiently high level, depending on the relative strength of attraction and alignment, the model is able to produce a cohesive fish school that replicates the main collective phases observed in nature: schooling, milling and swarming when each individual interacts with only one neighbor, and schooling and swarming when each individual interacts with two neighbors. Moreover, the model shows that these patterns can be maintained over long simulations. However, with the exception of swarming, these patterns do not persist indefinitely in time and fish lose cohesion and progressively disperse. We further identify the mechanisms leading to the dispersion.

animal behavior and cognition↗

bioRxiv2023

AI without networks

Contemporary Artificial Intelligence (AI) stands on two legs: large training data corpora and many-parameter artificial neural networks (ANNs). The data corpora are needed to represent the complexity and heterogeneity of the world. The role of the networks is less transparent due to the obscure dependence of the network parameters and outputs on the training data and inputs. This raises problems, ranging from technical-scientific to legal-ethical. We hypothesize that a transparent approach to machine learning is possible without using networks at all. By generalizing a parameter-free, statistically consistent data interpolation method, which we analyze theoretically in detail, we develop a network-free framework for AI incorporating generative modeling. We demonstrate this framework with examples from three different disciplines - ethology, control theory, and mathematics. Our generative Hilbert framework applied to the trajectories of small groups of swimming fish outperformed state-of-the-art traditional mathematical behavioral models and current ANN-based models. We demonstrate pure data interpolation based control by stabilizing an inverted pendulum and a driven logistic map around unstable fixed points. Finally, we present a mathematical application by predicting zeros of the Riemann Zeta function, achieving comparable performance as a transformer network. We do not suggest that the proposed framework will always outperform networks as over-parameterized networks can interpolate. However, our framework is theoretically sound, transparent, deterministic, and parameter free: remarkably, it does not require any compute-expensive training, does not involve optimization, has no model selection, and is easily reproduced and ported. We also propose an easily computed method of credit assignment based on this framework, to help address ethical-legal challenges raised by generative AI.

neuroscience↗

bioRxiv2023

Cooperation and deception through stigmergic interactions in human groups

Stigmergy is a generic coordination mechanism widely used by animal societies, in which traces left by individuals in a medium guide and stimulate their subsequent actions. In humans, new forms of stigmergic processes have emerged through the development of online services that extensively use the digital traces left by their users. Here we combine interactive experiments with faithful data-based modeling to investigate how groups of individuals exploit a simple rating system and the resulting traces in an information search task in competitive or non-competitive conditions. We find that stigmergic interactions can help groups to collectively find the cells with the highest values in a table of hidden numbers. We show that individuals can be classified into three behavioral profiles that differ in their degree of cooperation. Moreover, the competitive situation prompts individuals to give deceptive ratings and reinforces the weight of private information versus social information in their decisions.

animal behavior and cognition↗

bioRxiv2023

Tuning social interactions' strength drives collective response to light intensity in schooling fish

Schooling fish heavily rely on visual cues to interact with neighbors and avoid obstacles. The availability of sensory information is influenced by environmental conditions and changes in the physical environment that can alter the sensory environment of the fish, which in turn affects individual and group movements. In this study, we combine experiments and data-driven modeling to investigate the impact of varying levels of light intensity on social interactions and collective behavior in rummy-nose tetra fish. The trajectories of single fish and groups of fish swimming in a tank under different lighting conditions were analyzed to quantify their movements and spatial distribution. Interaction functions between two individuals and the fish interaction with the tank wall were reconstructed and modeled for each light condition. Our results demonstrate that light intensity strongly modulates social interactions between fish and their reactions to obstacles, which then impact collective motion patterns that emerge at the group level.

animal behavior and cognition↗

bioRxiv2023

Predicting long-term collective animal behavior with deep learning

Deciphering the social interactions that govern collective behavior in animal societies has greatly benefited from advancements in modern computing. Computational models diverge into two kinds of approaches: analytical models and machine learning models. This work introduces a deep learning model for social interactions in the fish species Hemigrammus rhodostomus, and compares its results to experiments and to the results of a state-of-the-art analytical model. To that end, we propose a systematic methodology to assess the faithfulness of a model, based on the introduction of a set of stringent observables. We demonstrate that machine learning models of social interactions can directly compete against their analytical counterparts. Moreover, this work demonstrates the need for consistent validation across different timescales and highlights which design aspects critically enables our deep learning approach to capture both short- and long-term dynamics. We also show that this approach is scalable to other fish species.

animal behavior and cognition↗

bioRxiv2021

The impact of individual perceptual and cognitive factors on collective states in a data-driven fish school model

In moving animal groups, social interactions play a key role in the ability of individuals to achieve coordinated motion. However, a large number of environmental and cognitive factors are able to modulate the expression of these interactions and the characteristics of the collective movements that result from these interactions. Here, we use a data-driven fish school model (Calovi et al., 2018; Lei et al., 2020) to quantitatively investigate the impact of perceptual and cognitive factors on coordination and collective swimming patterns. The model describes the interactions involved in the coordination of burst-and-coast swimming in groups of Hemigrammus rhodostomus. We perform a comprehensive investigation of the respective impacts of two interactions strategies between fish based on the selection of the most or the two most influential neighbors, of the range and intensity of social interactions, of the intensity of individual random behavioral fluctuations, and of the group size, on the ability of groups of fish to coordinate their movements. We find that fish are able to coordinate their movements when they interact with their most or two most influential neighbors, provided that a minimal level of attraction between fish exist to maintain group cohesion. A minimal level of alignment is also required to allow the formation of schooling and milling. However, increasing the strength of social interactions does not necessarily enhance group cohesion and coordination. When attraction and alignment strengths are too high, or when the heading random fluctuations are too large, schooling and milling can no longer be maintained and the school switches to a swarming phase. Increasing the interaction range between fish has a similar impact on collective dynamics as increasing the strengths of attraction and alignment. Finally, we find that coordination and schooling occurs for a wider range of attraction and alignment strength in small group sizes. Author SummaryIn fish schools, social interactions allow individuals to coordinate their movements and their modulation shape the emergent patterns of collective behavior. Here, we use a data-driven fish school model to investigate the impact of perceptual and cognitive factors on collective swimming patterns in the rummy-nose tetra (H. rhodostomus). In this species, fish only pay attention to one or two neighbors that exert the largest influence on their behavior and the interactions consist for a fish to be attracted and aligned with these neighbors. We show that there must exist a minimum level of alignment and attraction between fish to maintain group cohesion and allow the emergence of schooling and milling. Moreover, increasing the interaction range has a similar impact on collective dynamics as increasing the strength of social interactions. However, when the intensity of these interactions becomes too strong, fish can no longer coordinate their swimming and the school adopts a swarming behavior. Our results also show that a moderate level of behavioral fluctuations in fish can induce spontaneous transitions between schooling and milling. Finally, in this species that performs burst-and-coast swimming, we find that coordination occurs for a wider range of interaction strengths only in small group sizes.

animal behavior and cognition↗

bioRxiv2021

Computational modelling of cell motility modes emerging from cell-matrix adhesion dynamics

Lymphocytes have been described to perform different motility patterns such as Brownian random walks, persistent random walks, and Levy walks. Depending on the conditions, such as confinement or the distribution of target cells, either Brownian or Levy walks lead to more efficient interaction with the targets. The diversity of these motility patterns may be explained by an adaptive response to the surrounding extracellular matrix (ECM). Indeed, depending on the ECM composition, lymphocytes either display a floating motion without attaching to the ECM, or sliding and stepping motion with respectively continuous or discontinuous attachment to the ECM, or pivoting behaviour with sustained attachment to the ECM. Moreover, on the long term, lymphocytes either perform a persistent random walk or a Brownian-like movement depending on the ECM composition. How the ECM affects cell motility is still incompletely understood. Here, we integrate essential mechanistic details of the lymphocyte-matrix adhesions and lymphocyte intrinsic cytoskeletal induced cell propulsion into a Cellular Potts model (CPM). We show that the combination of de novo cell-matrix adhesion formation, adhesion growth and shrinkage, adhesion rupture, and feedback of adhesions onto cell propulsion recapitulates multiple lymphocyte behaviours, for different lymphocyte subsets and various substrates. With an increasing attachment area and increased adhesion strength, the cells speed and persistence decreases. Additionally, the model can predict short-term persistent with long-term subdiffusive motility, showing a pivoting motion. For small adhesion areas, we observe that the spatial distribution of adhesions influences cell motility. Small adhesions at the front allow for more persistent motion than larger clusters at the back, despite a similar total adhesion area. In conclusion, we present an integrated framework to simulate the effects of ECM proteins on cell-matrix adhesion dynamics. The model reveals a sufficient set of principles explaining the plasticity of lymphocyte motility. Author summaryDuring immunosurveillance, lymphocytes patrol through tissues to interact with cancer cells, other immune cells, and pathogens. The efficiency of this process depends on the kinds of trajectories taken, ranging from simple Brownian walks to Levy walks. The composition of the extracellular matrix (ECM), a network of macromolecules, affects the formation of cell-matrix adhesions, thus strongly influencing the way lymphocytes move. Here, we present a model of lymphocyte motility driven by adhesions that grow, shrink and rupture in response to the ECM and cellular forces. Compared to other models, our model is computationally light making it suitable for generating long term cell track data, while still capturing actin dynamics and adhesion turnover. Our model suggests that cell motility is affected by the force required to break adhesions and the rate at which new adhesions form. Adhesions can promote cell protrusion by inhibiting retrograde actin flow. After introducing this effect into the model, we found that it reduces the cellular diffusivity and that it promotes stick-slip behaviour. Furthermore, location and size of adhesion clusters determined cell persistence. Overall, our model explains the plasticity of lymphocyte behaviour in response to the ECM.

cell biology↗

bioRxiv2019

A data-driven method for reconstructing and modelling social interactions in moving animal groups

Group-living organisms that collectively migrate range from cells and bacteria to human crowds, and include swarms of insects, schools of fish and flocks of birds or ungulates. Unveiling the behavioural and cognitive mechanisms by which these groups coordinate their movements is a challenging task. These mechanisms take place at the individual scale and they can be described as a combination of pairwise interactions between individuals and interactions between these individuals and the physical obstacles in the environment. Thanks to the development of novel tracking techniques that provide large and accurate data sets, the main characteristics of individual and collective behavioural patterns can be quantified with an unprecedented level of precision. However, in a large number of works, social interactions are usually described by force map methods that only have a limited capacity of explanation and prediction, being rarely suitable for a direct implementation in a concise and explicit mathematical model. Here, we present a general method to extract the interactions between individuals that are involved in the coordination of collective movements in groups of organisms. We then apply this method to characterize social interactions in two species of shoaling fish, the rummynose tetra (Hemigrammus rhodostomus) and the zebrafish (Danio rerio), which both present a burst-and-coast motion. The detailed quantitative description of microscopic individual-level interactions thus provides predictive models of the emergent dynamics observed at the macroscopic group-level. This method can be applied to a wide range of biological and social systems.

animal behavior and cognition↗

bioRxiv2019

Computational and robotic modeling reveal parsimonious combinations of interactions between individuals in schooling fish

Coordinated motion and collective decision-making in fish schools result from complex interactions by which individuals integrate information about the behavior of their neighbors. However, little is known about how individuals integrate this information to take decisions and control their movements. Here, we combine experiments with computational and robotic approaches to investigate the impact of different strategies for a fish to interact with its neighbors on collective swimming in groups of rummy-nose tetra (Hemigrammus rhodostomus). By means of a data-based agent model describing the interactions between pairs of H. rhodostomus (Calovi et al., 2018), we show that the simple addition of the pairwise interactions with two neighbors quantitatively reproduces the collective behavior observed in groups of five fish. Increasing the number of interacting neighbors does not significantly improve the simulation results. Remarkably, we find that groups remain cohesive and polarized even when each agent only interacts with only one of its neighbors: the one that has the strongest contribution to the heading variation of the focal agent. However, group cohesion is lost when each agent only interacts with its nearest neighbor. We then investigate by means of a swarm robotic platform the collective motion in groups of five robots. Our platform combines the implementation of the fish behavioral model and an engineering-minded control system to deal with real-world physical constraints. We find that swarms of robots are able to reproduce the behavioral patterns observed in groups of five fish when each robot only interacts with its neighbor having the strongest effect on its heading variation, whereas increasing the number of interacting neighbors does not significantly improve the group coordination. Overall, our results suggest that fish have to acquire only a minimal amount of information about their environment to coordinate their movements when swimming in groups. Author SummaryHow do fish combine and integrate information from multiple neighbors when swimming in a school? What is the minimum amount of information about their environment needed to coordinate their motion? To answer these questions, we combine experiments with computational and robotic modeling to test several hypotheses about how individual fish could combine and integrate the information on the behavior of their neighbors when swimming in groups. Our research shows that, for both simulated agents and robots, using the information of two neighbors is sufficient to qualitatively reproduce the collective motion patterns observed in groups of fish. Remarkably, our results also show that it is possible to obtain group cohesion and coherent collective motion over long periods of time even when individuals only interact with their most influential neighbor, that is, the one that exerts the most important effect on their heading variation.

animal behavior and cognition↗

bioRxiv2017

Domino-Like Propagation Of Collective U-Turns In Fish Schools

Moving animal groups such as schools of fish or flocks of birds often undergo sudden collective changes of their travelling direction as a consequence of stochastic fluctuations in heading of the individuals. However, the mechanisms by which these behavioural fluctuations arise at the individual level and propagate within a group are still unclear. In the present study, we combine an experimental and theoretical approach to investigate spontaneous collective U-turns in groups of rummy-nose tetra (Hemigrammus rhodostomus) swimming in a ring-shaped tank. U-turns imply that fish switch their heading between the clockwise and anticlockwise direction. We reconstruct trajectories of individuals moving alone and in groups of different sizes. We show that the group decreases its swimming speed before a collective U-turn. This is in agreement with previous theoretical predictions showing that speed decrease facilitates an amplification of fluctuations in heading in the group, which can trigger U-turns. These collective U-turns are mostly initiated by individuals at the front of the group. Once an individual has initiated a U-turn, the new direction propagates through the group from front to back without amplification or dampening, resembling the dynamics of falling dominoes. The mean time between collective U-turns sharply increases as the size of the group increases. We develop an Ising spin model integrating anisotropic and asymmetrical interactions between fish and their tendency to follow the majority of their neighbours nonlinearly (social conformity). The model quantitatively reproduces key features of the dynamics and the frequency of collective U-turns observed in experiments.

animal behavior and cognition↗