Detecting Regulatory States in Natural Language: A Validation Study of the Four Nervous System States Gradient

This study presents the first empirical validation of the Four Nervous System States Gradient (pre-registered as the "Four-Mode Gradient"), a theoretical framework proposing that human regulatory states can be classified into four states — Safety & Openness (Connection), Threat & Defence (Protection), Strategy & Management (Control), and Power & Dominance (Domination) — based on perceived safety or threat. The framework is grounded in a two-system architecture: an Emotional Somatic System (ESS) that generates physiological responses before conscious awareness, and a Cognitive-Logical System (CLS) that builds from whatever data reaches it. Safety & Openness and Threat & Defence are ESS-led states; Strategy & Management and Power & Dominance are states in which the CLS is recruited into threat organisation.

Using natural language analysis of 10,000+ Reddit "Am I The Asshole" (AITA) posts, we tested whether these four states could be detected using validated psychological constructs from polyvagal theory, attachment research, contempt markers (Gottman), and moral disengagement theory (Bandura).

Key findings:

• All four states successfully detected in natural language
• State classifications correlated with external community judgments
• 33.8% of individuals escalated toward Strategy & Management/Power & Dominance when challenged
• 22.2% de-escalated toward Safety & Openness
• Interoceptive Self-Awareness (SEA) — the bridge between ESS and CLS — differentiated trajectories
• De-escalators showed 78% higher complexity marker rates than escalators
• Threat & Defence (Protection) did not cluster separately from other states — confirmed as a structural prediction, not a measurement limitation

Critical discovery: The key variable is not current state but whether the interoceptive channel is open — capacity to return to Safety & Openness when challenged.

Version 2.0 documents how this study generated the ESS/CLS architectural distinction and the identification of SEA as a distinct biological capacity. The data came first. The precision followed.

Pre-registration: https://osf.io/f4x6y Framework: https://teg-blue.com DOI: 10.5281/zenodo.19472342