Identifying Biological, Socio-Emotional, and Experiential Risks That Foretell the Emergence of Callous-Unemotional (CU) Traits

FALL 2018 RESEARCH INCUBATION AWARDEE

PI: Nicholas Wagner, Assistant Professor, Psychological & Brain Sciences
Co-PI: Anthony Rosellini, Research Assistant Professor, Psychological & Brain Sciences

What is the Challenge?
Children with callous-unemotional (CU) traits, characterized by low empathy, guilt, and prosociality, are more likely to develop disruptive behavior disorder (DBD) and show severe and persistent aggression, violence, and rule-breaking across childhood and adolescence. Despite advances in our understanding of the factors that lead to the emergence of CU traits, approaches to assessing them in childhood are woefully inadequate, often relying on psychometrically unstable questionnaires. To further advance our understanding of CU traits, we urgently need assessments of CU traits that are accessible, reliable, and valid across clinical and community settings and provide insight into underlying risk mechanisms.

What is the Solution?
To use machine learning techniques to identify key predictors of CU traits in existing datasets and, guided by these findings, to test desktop-computer versions of experimental tasks which map onto the key variables identified in Step One. If funded, this proposal will provide critical pilot data in support of an NIMH grant proposal (PA-18-345 target submission date Oct. 15, 2020) focused on developing an accessible, interactive, and reliable assessment of CU traits, leveraging measurement across transdiagnostic and mechanistic risk markers.

What is the Process?
Once pilot data on influential variables has been collected, a final component will involve two or three consultation meetings with the Software & Application Innovation Lab (SAIL) to determine the feasibility of developing an innovative mobile electronic tool for assessing CU traits, the Electronic Assessment of Socioemotional and Interpersonal Emotions (EASIE), ultimately composed of both questionnaire items and a series of “game-style” mhealth modules.