Novel Computational Neuropsychiatry Model for Understanding Psychosis: Tonic–Phasic Dopamine Biregulation

Computational Neuropsychiatry Model of Psychosis via Tonic–Phasic Dopamine Biregulation This work introduces a computational psychiatry framework describing psychosis as a dynamical instability in dopaminergic regulation. The model formalizes the interaction between tonic dopamine baseline, phasic dopamine responses to environmental stimuli, and attentional reinforcement within a closed feedback loop. Conventional descriptions of psychosis frequently interpret dopaminergic dysfunction as a static excess of dopamine signaling. In contrast, the present framework models psychosis as a regulatory imbalance between tonic dopamine baseline and phasic dopamine responses to stimuli. When tonic dopamine regulation is insufficient, phasic dopamine responses become disproportionately amplified. Through attentional reinforcement, this imbalance produces a positive feedback loop that progressively increases the perceived salience of neutral stimuli. Once this regulatory imbalance crosses a cortical gating threshold associated with prefrontal inhibitory control, the system enters a persistent instability regime corresponding to psychotic salience attribution. The model integrates several established research traditions including aberrant salience theory, predictive coding frameworks, reinforcement learning models of dopamine signaling, and dynamical systems approaches to neural regulation. Mathematical Formulation The regulatory system is modeled as a coupled dynamical system describing tonic dopamine dynamics, phasic dopamine responses, and attentional reinforcement. Tonic dopamine regulation is represented as: dT/dt = κ − λT where T = tonic dopamine baselineκ = tonic dopamine restoration rateλ = metabolic decay constant Phasic dopamine responses are modeled as stimulus-driven bursts: P(t) = α S(t) where P(t) = phasic dopamine responseS(t) = environmental stimulus intensityα = stimulus sensitivity coefficient Attentional reinforcement is modeled as: dA/dt = β P − γ A where A = attentional salience amplificationβ = attentional gain factorγ = attentional decay rate Master Stability Condition The regulatory stability of the system depends on the relationship between phasic dopamine signaling and tonic dopamine baseline. Define the dopaminergic salience ratio: Φ = P / T Psychotic instability occurs when: Φ > θ where Φ = phasic–tonic dopamine ratioθ = cortical salience gating threshold When this threshold is crossed, the attentional feedback loop amplifies salience attribution, generating a persistent attractor state corresponding to psychotic perception. Main Findings The model proposes the following theoretical findings: • Psychosis can be interpreted as a dynamical instability in dopaminergic regulation, rather than a simple excess of dopamine signaling. • The ratio between phasic dopamine responses and tonic dopamine baseline functions as a regulatory control parameter determining system stability. • When phasic dopamine activity exceeds tonic regulatory capacity, attentional reinforcement creates a positive feedback loop that amplifies salience attribution. • This feedback loop can generate a runaway attractor state in which neutral environmental stimuli acquire pathological significance. • Psychotic symptom severity is predicted to correlate with the phasic–tonic dopamine ratio Φ. • Pharmacological strategies that restore tonic dopamine baseline may stabilize the system while preserving adaptive phasic reward signaling. • Conventional antipsychotic treatments primarily achieve stabilization through phasic dopamine suppression, which may reduce aberrant salience but can impair reinforcement learning and motivation. Empirical Testing Strategy The model proposes several empirical validation approaches: • PET imaging of tonic and stimulus-evoked dopaminergic signaling• functional neuroimaging of cortical salience and control networks• reinforcement learning behavioral tasks measuring salience attribution Hierarchical Bayesian parameter estimation may be used to estimate individual system parameters from multimodal data. Scope of the Model The present framework describes the maintenance dynamics of psychosis rather than its developmental origin. Genetic and neurodevelopmental factors are therefore outside the scope of the current formulation but may influence parameters such as tonic restoration rate or attentional amplification. The model can be extended across multiple dopaminergic pathways including: • mesolimbic pathway (reward and salience processing)• mesocortical pathway (executive regulation)• nigrostriatal pathway (motor function)• tuberoinfundibular pathway (hormonal regulation) File Description The uploaded document contains the full theoretical formulation of the tonic–phasic dopamine regulatory model, including the dynamical system equations, the master stability condition governing psychotic transition, theoretical analysis of attractor dynamics, and integration with existing computational neuropsychiatry literature. Author Evangelos-Konstantinos GeorgantasIndependent Researcher References Aberrant Salience and Dopamine Dysregulation Kapur, S. (2003). Psychosis as a state of aberrant salience: A framework linking biology, phenomenology, and pharmacology in schizophrenia. American Journal of Psychiatry, 160(1), 13–23. https://doi.org/10.1176/appi.ajp.160.1.13 Kapur, S., Mizrahi, R., & Li, M. 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