Identifying emotion subtypes from individualized emotion space

Abstract

Many investigations into emotion processing contend that emotions can be reduced to a set of lower dimensions (e.g., valence and arousal). Additionally, emotion dysregulation is associated with numerous psychiatric disorders, whose treatment(s) may require inspiration from personalized medicine. To translate emotion research to the clinical domain, one may therefore need to investigate at the individual level, employing datadriven methods and forgoing classical assumptions regarding emotions. To this end, we explored the relative structure of emotion information resulting from 85 participants organizing emotionally-charged images following their own emotional responses to the pictures. Using cluster analyses and multidimensional scaling, we investigated the underlying composition of individuals' emotion spaces. Hierarchical clustering revealed five subtypes that reflect differing layouts of the emotion space; multidimensional scaling of each subtype's representative emotion space demonstrated that, although valence explained the primary organization of all emotion spaces, arousal as a secondary explanatory variable played a reduced role differentially for the subtypes, suggesting intrinsic differences in emotion information processing. Such data-driven methods yield new, unbiased ways of studying emotions and may reveal limitations of classic models or idiosyncrasies of individuals, which can inform future neuroimaging research and offer new approaches for studying emotions and emotion dysfunctions in psychiatric disorders.

Linking Personality Traits to Individual Differences in Affective Spaces

“…The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation, to any qualified researcher upon reasonable request. A subset of the data presented in this manuscript were previously made public in a preprint at PsyArXiv (Levine et al, 2018a).…”