arXiv subjects

M. Carmen Miguel

Publications and source records attributed to M. Carmen Miguel.

14 recordsLinked to original sources

arXiv2026

Habituation determines spatial position in Hyphessobrycon herbertaxelrodi shoals through altered rules of interaction

Habituation plays a key role in shaping the collective behavior of moving animal groups, yet the effects of variation in habituation within groups are unknown. Here, we merge two subgroups of fish with different levels of habituation and quantitatively analyze their movement. Non-habituated fish (not familiar with their swimming environment) tend to occupy more central positions within the group and position themselves closer to neighbors, consistent with the selfish herd hypothesis. This behavior appears to be driven by adjusted effective local interactions with nearby individuals. Compared to more habituated fish, these individuals also exhibit greater coordination, shorter burst-and-coast dynamics, more efficient information transmission, and a stronger tendency to adopt follower roles. To validate the findings, we develop a machine learning tool that successfully classifies the subgroup identity of individual fish. Our study demonstrates how differences in local-scale inter-individual interactions can drive spatial assortment within groups, based on heterogeneity in their manipulated exposure to the experimental setup.

physics.bio-ph↗

arXiv2026

Phase synchronization of fish schools through spatial gaps

We investigate how sensory coupling mediates phase coherence between spatially separated, confined active systems. Using two fish schools separated by a water-filled transparent gap, we isolate vision as the primary interaction channel. Under boundary confinement, each school's center-of-mass heading exhibits noisy bistable dynamics, spontaneously switching between two antiparallel directions along the partition wall. Turning events in one school bias the orientation of the other, driving distance-dependent phase synchronization. We model this behavior using two coupled stochastic bistable oscillators whose exact solution quantitatively reproduces the empirical results. Our work establishes a precise connection between nonequilibrium active systems and coupled stochastic oscillators, offering insight into boundary-spanning information transfer in living collectives.

cond-mat.other↗

arXiv2026

Data-Driven Stochastic Modeling of Schooling Fish: From Collective Dynamics to Individual Fluctuations

Collective motion in animal groups emerges from the interplay between individual variability and social coordination, yet connecting these scales quantitatively has remained a major challenge.Using high-resolution trajectories of schooling fish, we infer a data-driven stochastic framework that reproduces with remarkable accuracy the behavior of real fish schools. We decompose motion into two coupled components: the dynamics of the school's center of mass (or centroid), modeled as an active Brownian particle confined by the tank, and individual motions relative to that center, described by stochastic equations with data-inferred mean-field potentials and multiplicative noise. Simulations of these equations produce synthetic schools that quantitatively match real ones across multiple observables, including burst-and-coast dynamics, polarization, and spatial cohesion. This minimal, predictive framework bridges experiment and theory, showing that the collective dynamics of animal groups can be faithfully reconstructed from first principles directly from data.

cond-mat.other↗

arXiv2025

Symmetry breaking in collective decision-making through higher-order interactions

Collective decision-making is a widespread phenomenon in both biological and artificial systems, where individuals reach a consensus through social interactions. While traditional models of opinion dynamics and contagion focus on pairwise interactions, recent research emphasizes the importance of including higher-order group interactions and autonomous behavior to better reflect real-world complexity. In this work, we introduce a collective decision-making model inspired by social insects. In our framework, uncommitted agents can explore options independently and become committed, while social interactions influence these agents to prefer options already accepted by the group. Our model extends classical contagion models by incorporating multiple, mutually exclusive options and distinguishing between pairwise and higher-order social influences. Using simulations and analytical mean-field solutions, we show that higher-order interactions are essential for breaking symmetry in systems with equally valid options. We find that pairwise communication alone can cause decision deadlock, but adding group interactions allows the system to overcome stalemates and reach consensus. Our results emphasize the important roles of autonomous behavior and higher-order structures in collective decision-making. These insights could help us better understand social systems and design decision protocols for artificial swarms.

physics.soc-ph↗

arXiv2025

Non-linear inhibitory responses enhance performance in collective decision-making

The precise modulation of activity through inhibitory signals ensures that both insect colonies and neural circuits operate efficiently and adaptively, highlighting the fundamental importance of inhibition in biological systems. Modulatory signals are produced in various contexts and are known for subtly shifting the probability of receiver behaviors based on response thresholds. Here we propose a non-linear function to introduce inhibitory responsiveness in collective decision-making inspired by honeybee house-hunting. We show that, compared with usual linear functions, non-linear responses enhance final consensus and reduce deliberation time. This improvement comes at the cost of reduced accuracy in identifying the best option. Nonetheless, for value-based tasks, the benefits of faster consensus and enhanced decision-making might outweigh this drawback.

cond-mat.dis-nn↗

arXiv2025

Intelligent Soft Matter: Towards Embodied Intelligence

Intelligent soft matter stands at the intersection of materials science, physics, and cognitive science, promising to change how we design and interact with materials. This transformative field seeks to create materials that possess life-like capabilities, such as perception, learning, memory, and adaptive behavior. Unlike traditional materials, which typically perform static or predefined functions, intelligent soft matter dynamically interacts with its environment. It integrates multiple sensory inputs, retains experiences, and makes decisions to optimize its responses. Inspired by biological systems, these materials intend to leverage the inherent properties of soft matter: flexibility, self-evolving, and responsiveness to perform functions that mimic cognitive processes. By synthesizing current research trends and projecting their evolution, we present a forward-looking perspective on how intelligent soft matter could be constructed, with the aim of inspiring innovations in fields such as biomedical devices, adaptive robotics, and beyond. We highlight new pathways for integrating design of sensing, memory and action with internal low-power operations and discuss challenges for practical implementation of materials with "intelligent behavior". These approaches outline a path towards to more robust, versatile and scalable materials that can potentially act, compute, and "think" by their inherent intrinsic material behaviour beyond traditional smart technologies relying on external control.

cond-mat.soft↗

arXiv2024

Consensus formation and relative stimulus perception in quality-sensitive, interdependent agent systems

We perform a comprehensive analysis of a collective decision-making model inspired by honeybee behavior. This model integrates individual exploration for option discovery and social interactions for information sharing, while also considering option qualities. Our assessment of the decision process outcome employs standard consensus metrics and investigates its correlation with convergence time, revealing common trade-offs between speed and accuracy. Furthermore, we show the model's compliance with Weber's Law of relative stimulus perception, aligning with previous analysis of collective decision problems. Our study also identifies non-equilibrium critical behavior in specific limits of the model, where the highest values of consensus are achieved. This result highlights the intriguing relationship between optimal performance, critically, and the fluctuations caused by finite size effects, often seen in biological systems. Our findings are especially relevant for finite adaptive systems, as they provide insights into navigating decision-making scenarios with similar options more effectively.

cond-mat.dis-nn↗

arXiv2024

Signatures of criticality in turning avalanches of schooling fish

Moving animal groups transmit information through propagating waves or behavioral cascades, exhibiting characteristics akin to systems near a critical point from statistical physics. Using data from freely swimming schooling fish in an experimental tank, we investigate spontaneous behavioral cascades involving turning avalanches, where large directional shifts propagate across the group. We analyze several avalanche metrics and provide a detailed picture of the dynamics associated to turning avalanches, employing tools from avalanche behavior in condensed matter physics and seismology. Our results identify power-law distributions and robust scale-free behaviour through data collapses and scaling relationships, confirming a necessary condition for criticality in fish schools. We explore the biological function of turning avalanches and link them to collective decision-making processes in selecting a new movement direction for the school. We report relevant boundary effects arising from interactions with the tank walls and influential roles of boundary individuals. Finally, spatial and temporal correlations in avalanches are explored using the concept of aftershocks from seismology, revealing clustering of avalanche events below a designated timescale and an Omori law with a faster decay rate than observed in earthquakes.

physics.bio-ph↗

arXiv2024

Honeybee-like collective decision making in a kilobot swarm

Drawing inspiration from honeybee swarms' nest-site selection process, we assess the ability of a kilobot robot swarm to replicate this captivating example of collective decision-making. Honeybees locate the optimal site for their new nest by aggregating information about potential locations and exchanging it through their waggle-dance. The complexity and elegance of solving this problem relies on two key abilities of scout honeybees: self-discovery and imitation, symbolizing independence and interdependence, respectively. We employ a mathematical model to represent this nest-site selection problem and program our kilobots to follow its rules. Our experiments demonstrate that the kilobot swarm can collectively reach consensus decisions in a decentralized manner, akin to honeybees. However, the strength of this consensus depends not only on the interplay between independence and interdependence but also on critical factors such as swarm density and the motion of kilobots. These factors enable the formation of a percolated communication network, through which each robot can receive information beyond its immediate vicinity. By shedding light on this crucial layer of complexity --the crowding and mobility conditions during the decision-making--, we emphasize the significance of factors typically overlooked but essential to living systems and life itself.

cond-mat.dis-nn↗

arXiv2024

Selective social interactions and speed-induced leadership in schooling fish

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 analysing 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 neighbours, and an unexpected anti-alignment with slower neighbours. Instead of an explicit anti-alignment behaviour, we suggest that the observed pattern is the result of a selective attention mechanism, where fish pay less attention to slower neighbours. This mechanism implies the existence of temporal leadership interactions based on relative speeds between neighbours. We present support for this hypothesis both from agent-based modelling, as well as from exploring leader-follower relationships in the experimental data.

physics.bio-ph↗

arXiv2022

Scale-free behavioral cascades and effective leadership in schooling fish

Behavioral contagion and the presence of behavioral cascades are natural features in groups of animals showing collective motion, such as schooling fish or grazing herbivores. Here we study empirical behavioral cascades observed in fish schools defined as avalanches of consecutive large changes in the heading direction of the trajectory of fish. In terms of a minimum turning angle introduced to define a large change, avalanches are characterized by distributions of size and duration showing scale-free signatures, reminiscent of self-organized critical behavior. We observe that avalanches are generally triggered by a small number of fish, which act as effective leaders that induce large rearrangements of the group's trajectory. This observation motivates the proposal of a simple model, based in the classical Vicsek model of collective motion, in which a given individual acts as a leader subject to random heading reorientations. The model reproduces qualitatively the empirical avalanche behavior observed in real schools, and hints towards a connection between effective leadership and avalanche behavior in collective movement.

nlin.AO↗

arXiv2020

Amplitude death and restoration in networks of oscillators with random-walk diffusion

We study the death and restoration of collective oscillations in networks of oscillators coupled through random-walk diffusion. Differently than the usual diffusion coupling used to model chemical reactions, here the equilibria of the uncoupled unit is not a solution of the coupled ensemble. Instead, the connectivity modifies both, the original unstable fixed point and the stable limit-cycle, making them node-dependent. Using numerical simulations in random networks we show that, in some cases, this diffusion induced heterogeneity stabilizes the initially unstable fixed point via a Hopf bifurcation. Further increasing the coupling strength the oscillations can be restored as well. Upon numerical analysis of the stability properties we conclude that this is a novel case of amplitude death. Finally we use a heterogeneous mean-field reduction of the system in order to proof the robustness of this phenomena upon increasing the system size.

nlin.AO↗

arXiv2019

Scalar model of flocking dynamics on complex social networks

We investigate the effects of long-range social interactions in flocking dynamics by studying the dynamics of a scalar model of collective motion embedded in a complex network representing a pattern of social interactions, as observed in several social species. In this scalar model we find a phenomenology analogous to that observed in the classic Vicsek model: In networks with low heterogeneity, a phase transition separates an ordered from a disordered phase. At high levels of heterogeneity, instead, the transition is suppressed and the system is always ordered. This observation is backed up analytically by the solution of a modified scalar model within an heterogeneous mean-field approximation. Our work extends the understanding of the effects of social interactions in flocking dynamics and opens the path to the analytical study of more complex topologies of social ties.

physics.soc-ph↗

arXiv2018

Effects of heterogeneous social interactions on flocking dynamics

Social relationships characterize the interactions that occur within social species and may have an important impact on collective animal motion. Here, we consider a variation of the standard Vicsek model for collective motion in which interactions are mediated by an empirically motivated scale-free topology that represents a heterogeneous pattern of social contacts. We observe that the degree of order of the model is strongly affected by network heterogeneity: more heterogeneous networks show a more resilient ordered state; while less heterogeneity leads to a more fragile ordered state that can be destroyed by sufficient external noise. Our results challenge the previously accepted equivalence between the {\em static} Vicsek model and the equilibrium XY model on the network of connections, and point towards a possible equivalence with models exhibiting a different symmetry.

physics.soc-ph↗