Computational mechanisms of gratitude practice

Positive psychology interventions, such as gratitude practice, claim to substantially improve an individual’s well-being. By focusing on three things one is grateful for each day, gratitude practice may be a promising simple and scalable intervention to support well-being. However, there is mixed evidence of the efficacy of gratitude practice and we lack a mechanistic understanding of the underlying processes. 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 results. The first is an instantiation of gratitude practice as the deliberate deployment of high precision (i.e. attention) of three good observations throughout the day. We demonstrate theoretically how agents form their beliefs about their world from the environment they are in, and how these beliefs change positively after gratitude practice. To understand the impact on the agent’s worldview and to connect the modelling results to the empirical literature, we simulate an optimism assessment task before and after the intervention, showing an increase in optimism after gratitude practice. Finally, we simulate a modified version of the Yarbus eye movement task and find that gratitude practice may affect habitual attention patterns, such that agents attend to more positive elements compared to neutral or negative elements in a painting after gratitude practice. 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, and why some individuals may not.