Ryan Oisín, Dablander Fabian, Haslbeck Jonas M B
Department of Data Science and Biostatistics, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University.
Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam.
Psychol Rev. 2025 Mar;132(2):416-441. doi: 10.1037/rev0000513. Epub 2024 Dec 30.
Most theories of emotion suggest that emotions are reactions to situations we encounter in daily life. Process theories of emotion further specify a feedback loop between our environment, attention, emotions, and action that clarifies the adaptive nature of emotions. In principle, such process theories describe how emotions develop in daily life, and consequently, emotion measures collected from individuals many times a day in studies using the experience sampling methodology should be highly useful in advancing these theories. However, current emotion theories are predominantly verbal theories and therefore do not make clear predictions about such data. In this article, we take a first step toward a generative model of emotion dynamics by formalizing the link between situations and emotions, which provides us with a basic generative model of emotions in daily life. We show that this incomplete model already reproduces nine empirical phenomena in emotion time series related to (temporal) statistical associations between emotions and their distributional form. We then discuss how we can draw on existing (process) theories of emotion to extend our basic model into a complete generative model of emotion dynamics. Finally, we discuss how generative models of emotion dynamics can facilitate theory development and advance measurement, study design, and statistical analysis. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
大多数情绪理论认为，情绪是我们对日常生活中遇到的情境的反应。情绪的过程理论进一步明确了我们的环境、注意力、情绪和行动之间的反馈回路，阐明了情绪的适应性本质。原则上，这类过程理论描述了情绪在日常生活中的发展方式，因此，在使用经验抽样方法的研究中，每天多次从个体收集的情绪测量数据，应该对推进这些理论非常有用。然而，当前的情绪理论主要是文字理论，因此并未对这类数据做出明确的预测。在本文中，我们通过形式化情境与情绪之间的联系，朝着情绪动态生成模型迈出了第一步，这为我们提供了日常生活中情绪的基本生成模型。我们表明，这个不完整的模型已经再现了情绪时间序列中与情绪及其分布形式之间的（时间）统计关联相关的九种实证现象。然后，我们讨论如何借鉴现有的情绪（过程）理论，将我们的基本模型扩展为一个完整的情绪动态生成模型。最后，我们讨论情绪动态生成模型如何促进理论发展，并推进测量、研究设计和统计分析。（PsycInfo数据库记录（c）2025美国心理学会，保留所有权利）
