A network analytic approach to measuring emotional granularity

Emotional granularity describes an individual’s ability to create instances of emotion that are diverse and context-specific. Considerable evidence suggests that higher emotional granularity is a protective factor for mental and physical well-being. In the present study, we examined emotional granularity using a network analytic approach that produced multi-dimensional, time-varying measures. Individuals with higher granularity exhibited person-specific emotion networks that were less dense, comprised of a greater number of communities of emotions, and organized by a greater diversity of connections. Individuals with higher granularity also exhibited greater network change over time. Network measures of emotional granularity predicted self-reported anxiety and depression, even when controlling for other variables known to be associated with mood symptoms, such as self-reported alexithymia and emotional reactivity. Taken together, these findings serve as a proof-of-concept demonstration of the efficiency of network analysis for describing the dynamic structure of emotional experience.