Relationship dynamics between anxious and avoidant attachment styles: computational analysis and AI-generated video creation

This paper introduces a two-agent model to simulate conflict between an anxiously attached partner (Agent A) and an avoidantly attached partner (Agent B). The agent model is based on attachment theory and integrates elements from affective neuroscience and adaptive dynamical systems for showing how internal working models, emotional regulation and interpersonal feedback influence relational conflict. The agent model was designed as a multi-order adaptive architecture with states that represent emotional, behavioral and regulatory components. The agent model learns from co-activated relational experiences. The simulation explores the fight phase of conflict escalation which demonstrates specific patterns that occur when anxious and avoidant partners interact. Agent A shows quick sensory reactivity, schema activation and protest preparation, but Agent B shows emotional shutdown and belief-driven withdrawal. The agent model demonstrates certain interaction mechanisms between partners where distress from one partner strengthens defensive behaviors in the other partner which matches with observed relational patterns from empirical research. The addressed scenario and related simulation outcomes have been used as scripts for AI-generated videos to show the patterns in an illustrative manner. The research demonstrates how at a detailed level adaptive network modeling of the underlying dynamical systems helps to establish more insight and knowledge about attachment-based interpersonal behavior and when displayed in a relatable manner could lead to future therapeutic simulation development.