Neural Reward Processing in Digital Addiction: A Dynamical Systems Theory of Reward Instability

Area of Science:

• Neurobehavioral Science
• Dynamical Systems Theory
• Digital Environment Research

Background:

• Behavioral addiction is a growing concern in digital settings, driven by optimized reward stimuli.
• Existing neural models inadequately explain how modern digital environments alter behavior systemically.
• A new conceptual framework is needed to understand addiction in these complex systems.

Purpose of the Study:

• Introduce Reward Instability Theory (RIT) as a dynamical systems framework for behavioral addiction.
• Explain how digital environments with high-density, high-variance rewards may foster addiction.
• Propose the Behavioral Reward Instability Index (BRII) to quantify these dynamics.

Main Methods:

• Conceptual review and theoretical model development.
• Integration of reinforcement learning, salience, executive control, and reward structure.
• Discussion of digital phenotyping as an empirical strategy.

Main Results:

• RIT posits addiction emerges as an attractor-like state in distorted digital reward landscapes.
• Digital environments may increase reward density and variance, reducing behavioral diversity.
• The BRII integrates individual sensitivity, environmental structure, and behavioral variability.

Conclusions:

• RIT offers a systems-level account of motivation and behavioral persistence in digital addiction.
• The theory accommodates existing addiction models within a unified dynamical architecture.
• Further research using digital phenotyping is needed, acknowledging current limitations.