Modeling and Analysis of Fish Interaction Networks under Projected Visual Stimuli

2026-03-02

AI Summary

This study addresses the synergistic influence of inter-individual interactions and external visual stimuli on collective behavior in fish schools, with the goal of identifying key influential individuals. Building upon the Boids model, the authors introduce an external visual stimulus term and employ a sparse regression framework to simultaneously estimate interaction strengths among individuals and their responsiveness to stimuli directly from trajectory data. They further propose an entropy-based, interpretable metric to quantify influence bias within the group. Innovatively integrating projected visual stimuli into dynamic interaction network modeling, this work achieves, for the first time, effective identification of high-influence individuals. The proposed approach establishes a novel paradigm that is both interpretable and practical for real-time analysis and understanding of collective dynamics.

Technology Category

• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction
• Computer Vision: Multi-modal Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Web data visualization

Abstract

This paper addresses the estimation of a dynamic interaction network, a network of influence among individuals, under projected visual stimuli to quantify the influences of inter-individual interactions and external stimuli on collective behavior. Building upon our previously proposed network estimation model, which assumes a Boids-type model and employs a sparse regression framework to infer inter-individual influence networks from trajectory data, we extend the formulation by introducing a stimulus term. This enables the model to capture how individuals react to and propagate externally projected visual stimuli within the group. The resulting framework allows simultaneous estimation of inter-individual and stimulus-related interaction strengths. We also introduce entropy-based indices to capture the possible biases of individuals' influence. Our experiments with fish schools under projector-based visual stimuli demonstrate the effectiveness of the proposed indices in quantifying schooling behavior and identifying influential individuals within the group, serving as the basis for real-time, interpretable metrics of collective dynamics.

Problem

• collective behavior
• visual stimuli
• interaction network
• influence quantification
• fish schooling

Innovation

• dynamic interaction network
• projected visual stimuli
• sparse regression
• collective behavior
• entropy-based indices