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Toward a generative model for emotion dynamics.

Oisín Ryan¹, Fabian Dablander², Jonas M B Haslbeck³

¹Department of Data Science and Biostatistics, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University.

Psychological Review

|March 27, 2025

Summary

This summary is machine-generated.

This study introduces a basic generative model of emotion dynamics, linking situations to emotions. The model successfully reproduces nine empirical phenomena in emotion time series, advancing emotion theory and measurement.

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Area of Science:

Psychology
Computational Neuroscience
Affective Science

Background:

Emotion theories often describe emotions as reactions to life events.
Process theories highlight a feedback loop between environment, attention, emotions, and action.
Existing theories are primarily verbal, lacking clear predictions for intensive longitudinal data.

Purpose of the Study:

To develop a formal, generative model of emotion dynamics.
To bridge the gap between verbal emotion theories and quantitative data from experience sampling.
To provide a foundation for a complete generative model of emotion dynamics.

Main Methods:

Formalizing the relationship between situations and emotions.
Developing a basic generative model of emotion dynamics.
Testing the model against empirical phenomena in emotion time series data.

Main Results:

The incomplete generative model reproduced nine empirical phenomena in emotion time series.
These phenomena relate to temporal statistical associations and distributional forms of emotions.
Demonstrates the utility of a formal approach to modeling emotion dynamics.

Conclusions:

Generative models offer a powerful framework for advancing emotion theory.
This approach can enhance emotion measurement, study design, and statistical analysis.
Further development can lead to a complete generative model of emotion dynamics.