A Global Affective State Variable for Consciousness?Based Models and Digital Neuropsychiatry

Contemporary neurology and psychiatry rely on diverse mood and symptom scales, yet most affective measures remain post hoc and weakly tied to explicit models of consciousness. This limits cross?study comparability, integration with computational frameworks, and translation into digital biomarkers. The States of Mind (SOM) framework addresses this gap by deriving a single affective state variable from a formal reflexive architecture of consciousness. Building on Lefebvre’s algebraic model of self?awareness, SOM represents positive and negative evaluations of self and other across three levels of reflection, yielding an emotional balance ratio PPN bounded between 0.00 and 1.00.

Boolean computations over this architecture generate a small set of discrete balance?point states (approximately .50, .63, .72, .81, .88) corresponding to dysfunctional, coping, normal, optimal, and peak psychological functioning. These balance points are produced by the model itself rather than inferred from empirical curve fitting, and have shown convergent support across clinical trials, longitudinal case studies, and cross?cultural samples. In computational terms, SOM is proposed to function as a global affective readiness?to?respond variable. Within this framework, higher SOM balances (optimal, peak) are hypothesized to favor exploratory, opportunity?seeking policies, whereas lower balances (subnormal, dysfunctional) are hypothesized to constrain behavior and promote risk?avoidant or threat?focused policies.

This presentation outlines the SOM architecture, reviews key empirical findings, and illustrates how the SOM state variable can serve as a portable affect index for neurology and neuropsychiatry. Because SOM is measure?agnostic—it can be instantiated wherever positive and negative affect are assessed—it is readily implementable as a candidate digital biomarker in self?monitoring platforms, mobile applications, and wearable devices. We discuss implications for individualized tracking of treatment response, resilience, and relapse risk, and for integrating affect more rigorously into AI?assisted neurology and brain?inspired systems.

To be updated shortly..