Year: 2025
Type: preprint
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 (M s ) 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-µ plane.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.
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: gold
OpenAlex ID: W4413129684