Computational Mechanisms of Gratitude Practice

Abstract Positive psychology interventions, such as gratitude practice, aim to improve well-being through simple techniques. One widely used practice involves reflecting daily on three things one is grateful for. Although this practice is promising, empirical evidence for its efficacy is mixed, and the underlying mechanisms remain poorly understood. Here, we develop a computational model of gratitude practice to gain insight into the potential mechanisms of the practice. Employing the active inference framework, we present three simulation studies. First, we formalize gratitude practice as the deliberate allocation of high precision (i.e., attention) to three positive observations. This allows us to show how agents form beliefs about their environment and how these beliefs shift following gratitude practice. Second, to link the model to empirical findings, we simulate an optimism assessment task before and after the intervention, demonstrating an increase in optimism following gratitude practice. Finally, we simulate an abstract proof-of-concept task inspired by the Yarbus paradigm to model attention and find that gratitude practice may affect habitual attention patterns, where agents attend to positive elements over neutral or negative elements in a painting. Our model provides a conceptual understanding of gratitude practice that can be used in future research to gain insights into who may benefit from gratitude practice.