From Metaphor to Computation: Constructing the Potential Landscape for Multivariate Psychological Formal Models

Area of Science:

• Psychology
• Computational modeling
• Mathematical psychology

Background:

• Stability of phases is crucial for understanding psychological dynamics and individual differences.
• Current methods lack quantitative approaches to assess phase stability in psychological models.
• Potential landscapes are used metaphorically but lack formal quantification methods.

Purpose of the Study:

• To propose a novel method for constructing potential landscapes in multivariate psychological models.
• To define and quantify three types of stability: absolute, relative, and geometric.
• To demonstrate the application of the method using a panic disorder model.

Main Methods:

• Utilizing a generalized potential function and Monte Carlo simulation.
• Constructing potential landscapes for psychological models.
• Defining stability metrics based on the derived potential landscapes.

Main Results:

• Successfully quantified phase stability in a psychological model.
• Illustrated the impact of model parameters on stability.
• Provided a framework for guiding model modifications based on stability analysis.

Conclusions:

• The proposed method offers a quantitative approach to assess phase stability in psychological models.
• This quantification aids in understanding individual differences and psychological change.
• The developed R package, simlandr, facilitates the practical application of this method.