social-spatial-navigation-phase-transitions

name	social-spatial-navigation-phase-transitions
description	Social-spatial dependencies in visual navigation learning with neural network agents. Demonstrates phase transitions from individual to social following strategies based on information quality and spatial effects.
trigger_words	["social navigation","visual navigation","behavioral strategy","phase transition","social dependency","collision avoidance","behavioral hybridization"]
category	neuroscience

Social-Spatial Dependencies for Learning Visual Navigation

Core Methodology

Research Focus: How social structure and embodied interactions influence navigation behavior in social organisms

Key Innovation: Demonstrates phase transitions in navigational strategy based on social information quality and spatial context

Technical Framework

• Agent Architecture: Individual neural network controlled agents
• Training Context: Different social contexts with varying social dependence
• Strategy Determination: Based on relative task performance and spatial effect

Key Findings

1. Phase Transitions in Behavioral Strategy:

   • Increasing high-quality social information drives transitions:
     • Individual navigation → Following strategy
     • Following → Collision avoidance (crowded foraging patch)

2. Behavioral Hybridization:

   • Predictable, nonstationary environmental dynamics
   • Drives hybridization between individual and social navigation
   • Occurs both far and near resource patches

3. Spatial Context Effects:

   • Social dependence determined by spatial effects
   • Task performance influences strategy selection
   • Environmental dynamics modulate behavioral flexibility

Experimental Design

• Agents: Neural network controlled individuals
• Environments: Varied social contexts
• Metrics: Task performance, spatial effects, strategy transitions
• Analysis: Phase transition identification, behavioral hybridization detection

Theoretical Implications

1. Bottom-Up Approach: Challenges inspecting only individual behavior for social organisms
2. Emergent Properties: Social structure emerges from local interactions
3. Adaptive Flexibility: Agents dynamically switch strategies based on context

Applications

• Multi-agent robotics
• Swarm intelligence
• Social behavior modeling
• Adaptive navigation systems
• Collective decision-making

Key Insights

• Social information quality is critical for strategy transitions
• Spatial context modulates social vs. individual behavior
• Environmental predictability enables behavioral hybridization
• Phase transitions are not binary but continuous

Reference

arXiv:2607.07460v1 (July 8, 2026) Category: cs.NE (Neural and Evolutionary Computing)