A Machine Learning Approach to Relationships Among Alexithymia Components

The aim of this paper is to explore the network structures of alexithymia components and compare results with relevant prior literature.

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.

Alexithymia components present positive as well as negative connections; poor fantasy and emotional insight are identified as central items in the network.

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.