Modeling intraindividual variability in affect (MIVA): Formalized theoretical approach, computational model, and...

Area of Science:

• Psychology
• Computational Neuroscience
• Affective Science

Background:

• Affective experience is dynamic, fluctuating throughout daily life.
• Studying these changes offers insights into psychological functioning and well-being.
• Existing models may not fully capture the complexity of intraindividual affect variability.

Purpose of the Study:

• To introduce a formalized model of intraindividual variability in affect (MIVA).
• To outline the theoretical background, scope, and mathematical formulation of MIVA.
• To demonstrate the utility of MIVA for studying affect dynamics.

Main Methods:

• Developed a parsimonious formalized model of intraindividual variability in affect (MIVA).
• Used simulation-based inference with probabilistic neural networks to train a custom neural network.
• Employed the trained network for rapid parameter estimation on synthetic experiments.

Main Results:

• The synthesis of MIVA with probabilistic neural networks provides an efficient tool for affect dynamics inference.
• Simulation studies revealed data requirements for precise parameter recovery.
• The approach offers flexibility for analyzing various configurations of synthetic experiments.

Conclusions:

• MIVA is a valuable tool for understanding the ebb and flow of affective experience.
• The computational approach enhances the study of psychological functioning and well-being.
• Findings provide recommendations for future data collection in affect dynamics research.