A Dynamical Model of Subjectivity: Integrating Affective Gain, Cognitive Bias, and Self-Regulation

Background: A central challenge in affective and cognitive science is to explain how affective gain (G) and cognitive bias (mu) dynamically shape subjective experience. Although both constructs are widely studied, the mechanisms of their interaction—and their role in individual differences in mood and cognition—remain poorly understood.Methods: We developed a control-theoretic dynamical-systems model of the subjective state (Ms) that formalizes the coupling between G and mu. Bifurcation analysis of a potential function V(Ms) yields a Mind Topography Map, a global portrait of stability regimes across the G–mu plane. A higher-order Self-System adaptively navigates this landscape by regulating G and mu through hierarchical Bayesian learning.Results: Canonical cusp (saddle-node) and pitchfork bifurcations organize the landscape, generating qualitative shifts that correspond to psychological phenomena ranging from stable belief convergence to cognitive (attitude) polarization (bistability) and mood-like oscillations. We identified an Ideal Dynamical Equilibrium (G = 1, mu = 0) as an optimal balance of stability and responsiveness. An exploratory Structural Gain–Bias Dynamics (SGBD) extension represents person-specific traits with structural matrices, capturing unique “gain fingerprints” and mixed-emotion states.Conclusions: By unifying dynamical-systems analysis with agentic self-regulation, our framework clarifies core subjective dynamics through its integrated treatment of gain–bias coupling and its governing bifurcations. This provides a tractable route to personalized modeling and yields mathematically precise, falsifiable hypotheses for longitudinal self-report, experience-sampling, and neurophysiological studies. It thus offers a robust theoretical tool for computational psychology, affective science, psychiatry, and human-centric AI.