From metaphor to computation: Constructing the potential landscape for the dynamics of panic disorder

In recent years, formal models are gaining momentum in the field of psychology, aimed to address the theory crisis and provide a quantitative foundation for theoretical inferences. These formal models are often used to study psychological states, for example, a healthy state and a psychopathological state. The relative stability of these psychological states is an important avenue for research to better understand individual differences and (clinical) change processes. The potential landscape is often used as a metaphor to conceptually illustrate stability. But recently the corresponding numerical method has been developed, which provides a novel approach to gain quantitative insights into the stability of various states in psychological systems.
In this project, we use Wang’s method to quantitatively compute the landscape function for a formal dynamic model of panic disorder. Besides developing a general method, we also aim to examine the stable states of the model based on the landscape function. First results show one stable state – the healthy state – and one quasi-stable state – the panic state. We speculate that the quasi-stable panic state is due to the excitability of the considered model system, which shows longer residence time but not a metastable state. We further found that the stability of the panic state increases as the arousal schema becomes more sensitive. These results are in line with previous clinical findings, providing evidence for the validity of the model. In addition, we also investigated how other model variables and parameter settings influence the relative stability of different psychological states.