Defensive Motivational Nodes (DMN): Inferring Psychological States from Language via Defense Mechanisms and Affective motivation

This preprint introduces the Defensive Motivational Nodes (DMN) framework, a novel psychological-AI model designed to identify and interpret the defensive and motivational undercurrents embedded in human language. Drawing from cognitive neuroscience, clinical psychology, and computational linguistics, DMN classifies linguistic expressions into patterns of defense mechanisms (e.g., rationalization, displacement, humor) and emotional motivations (e.g., need for safety, autonomy, recognition). By mapping these layers simultaneously, the framework enables AI systems to engage in deep listening — attending not only to the explicit content of speech but also to the unspoken psychological context. Beyond AI applications, DMN offers a lens for improving human-to-human communication by fostering awareness of one’s own defensive tendencies and enhancing empathy toward others. This work represents an early step toward AI that can participate in emotionally intelligent interactions, with implications for mental health support, education, and cross-cultural communication.