A Machine Learning Approach to Relationships Among Alexithymia Components.

BACKGROUND: The aim of this paper is to explore the network structures of alexithymia components and compare results with relevant prior literature. SUBJECTS AND METHODS: In a large sample of university students, undirected and directed network structures of items from the Bermond Vorst Alexithymia Questionnaire form B are estimated with state-of-the-art network analysis and structure learning tools. Centrality estimates are used to address the topic of item redundancy and select relevant alexithymia components to study. RESULTS: Alexithymia components present positive as well as negative connections; poor fantasy and emotional insight are identified as central items in the network. CONCLUSIONS: The undirected network structure of alexithymia components reports new features with respect to prior literature, and the directed network structures offers new insight on the construct.