Emotion regulation, fast or slow: A computational model of strategy choice.

Abstract

Different emotion regulation strategies have very different consequences. This observation has inspired a growing body of work seeking to identify the factors that predict emotion regulation strategy choice. To explain these findings, several explanatory theories have been proposed. As with most theories in the field of affective science, they are formulated in natural language. Translating these theories into the language of mathematics may bring more clarity to the field and help generate new, testable hypotheses. The present article aimed to formulate more precise theoretical predictions by translating verbal theories about the emotion regulation selection process into formal mathematical language. Specifically, we focused on formally defining a theory that might help to explain the robust finding that people prefer distraction over reappraisal at high emotional intensities but prefer reappraisal over distraction at low emotional intensities. Through the process of theory formalization, we identified hidden assumptions and unanswered research questions, which resulted in a computational model that predicts results that match empirical work. This work demonstrates how theory formalization can accelerate theoretical and empirical progress in affective science. Better explanatory theories can then inform interventions designed to enhance the selection of adaptive regulation strategies. (PsycInfo Database Record (c) 2025 APA, all rights reserved).