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

A control-theoretic model formalizes how affective gain and cognitive bias interact to shape subjective experience. Bifurcation analysis of the model's potential function produces a Mind Topography Map showing stability regimes across the gain-bias plane. The landscape exhibits cusp and pitchfork bifurcations that generate qualitative shifts corresponding to stable belief convergence, cognitive polarization, and mood-like oscillations. An Ideal Dynamical Equilibrium at gain=1 and bias=0 represents optimal stability and responsiveness. An exploratory extension uses structural matrices to capture person-specific traits and mixed-emotion states. The framework unifies dynamical-systems analysis with agentic self-regulation, offering mathematically precise, falsifiable hypotheses for empirical studies.

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Abstract

Background: Explaining how affective gain (G) and cognitive bias (µ) dynamically interact to shape subjective experience is a central challenge in affective and cognitive science. While both constructs are widely studied, the mechanisms governing their interaction and role in individual differences remain poorly understood.Methods: We developed a control-theoretic dynamical-systems model of the subjective state (Ms) that formalizes G–µ coupling. Bifurcation analysis of the model’s potential function yields a Mind Topography Map, a global portrait of stability regimes across the G–µplane. A higher-order Self-System adaptively navigates this landscape by regulating G and µ via hierarchical Bayesian learning.Results: Canonical cusp and pitchfork bifurcations organize the landscape, generating qualitative shifts corresponding to psychological phenomena from stable belief convergence to cognitive polarization and mood-like oscillations. We identified an Ideal Dynamical Equilibrium (G= 1, µ= 0) as an optimal balance of stability and responsiveness. An exploratory extension (Structural Gain–Bias Dynamics; SGBD) represents person-specific traits with structural matrices to capture mixed-emotion states.Conclusions: By unifying dynamical-systems analysis with agentic self-regulation, our framework clarifies core subjective dynamics. It provides a tractable route to personalized modeling and yields mathematically precise, falsifiable hypotheses for empirical studies, including longitudinal and neurophysiological designs. Bridging concepts from Mathematical Psychology to control-theoretic frameworks like Perceptual Control Theory and the Free Energy Principle, our model offers a robust theoretical tool for computational psychology, affective science, and psychiatry.