Towards a Generative Model for Emotion Dynamics

Abstract

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 (ESM) should be highly useful in developing these theories. However, current emotion theories are predominantly verbal theories and therefore do not make clear predictions in such data. In this paper, we take a first step towards 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 stable phenomena in emotion time series related to (temporal) 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.

The Human Affectome

“…Indeed, both communication and thinking have felt aspect themselves ( Siewert, 2012 ; Siewert, 1998 ; Pitt, 2004 ; Bayne et al, 2014 ; Kind, 2001 ; Tulving, 1987 ; Roediger, 1990 ; Teroni, 2017 ; Gardiner and Java, 2019 ; Wharton et al, 2021 ). Instead, we should emphasize these abstract activities as active constructs in our picture of affective phenomena, such as by considering them determinants of relevance and, thus, generating activities of subjective report and communication as dynamic processes in themselves ( Wharton and de Saussure, 2023 ; Satpute et al, 2020 ; Ryan et al, 2023 ; Gohm and Clore, 2000 ; Robinson and Clore, 2002 ; Barrett, 2004 ; Cowen and Keltner, 2017 ; Russell, 1980 ; Watson et al, 1988 ; Bradley and Lang, 1994 ; Boehner et al, 2007 ; Wilhelm and Grossman, 2010 ; Betella and Verschure, 2016 ; Shiffman et al, 2008 ; Csikszentmihalyi and Larson, 2014 ; Kuppens et al, 2022 ; Betz et al, 2019 ; Satpute and Lindquist, 2021 ; Ericcson and Simon, 1993 ; Wilson, 1994 ; Schwarz, 1999 ; Dehaene et al, 2003 ; Mauss and Robinson, 2009 ; Barrett et al, 2011 ; Lindquist et al, 2015 ; Harmon-Jones et al, 2016 ; Rosenthal, 2019 ; Li et al, 2023 ; Teoh et al, 2023 ). In addition, we should acknowledge that we academics, as humans ourselves, are performing the very same activity of abstraction and—if we choose not to investigate ourselves as affect-ridden researchers—should at least acknowledge and disclose the assumptions from our pragmatic context and theoretical virtues (e.g., Scarantino, 2016 ).…”

Multimodality and skewness in emotion time series.

“…Each study also measured betweenperson characteristics of the participants relevant to their original study aims. In a review of the emotion time series literature detailed in Ryan et al (2023), we identified the following seven studies which shared their data without restrictions.…”

“…One prominent avenue may be HMMs, which allow the specification of different states and capture the probability of switching between states in a transition matrix (e.g., Catarino et al, 2020;Cochran et al, 2016;Dempsey et al, 2017;Hamaker et al, 2016;Prisciandaro et al, 2019;Visser & Speekenbrink, 2010;Yee et al, 2021). HMMs can reproduce multimodality and skewness in empirical data (Ryan et al, 2023). Another, often neglected avenue would be to focus on descriptive analyses instead of using advanced statistical models.…”