Objective Linguistic Markers Associated with Callous-Unemotional Traits in Early Childhood
Year: 2024
Type: article
Study design: Observational Study
Abstract: BACKGROUND: Callous-unemotional (CU) traits are associated with interpersonal difficulties and risk for severe conduct problems (CP). The ability to communicate thoughts and feelings is critical to social success, with language a promising treatment target. However, no prior studies have examined objective linguistic correlates of childhood CU traits in early childhood, which could give insight into underlying risk mechanisms and novel target treatments. METHODS: We computed lexical (positive emotion, sad, and anger words) and conversational (interruptions and speech rate) markers produced by 131 children aged 5-6 years (M = 5.98; SD = 0.54, 58.8% female) and their parents while narrating wordless storybooks during two online visits separated by 6-8 weeks (M = 6.56, SD = 1.11; two books, order counterbalanced). Audio recordings were diarized, time-aligned, and orthographically transcribed using WebTrans. Conversational markers were calculated using R and word frequencies were calculated using Linguistic Inquiry and Word Count (LIWC) software. We examined links between child CU traits and linguistic markers, and explored whether relationships were moderated by child sex. RESULTS: Higher CU traits were associated with fewer positive emotion words produced by parents and children. Higher CU traits were also associated with greater concordance in the degree of interruptions and expression of anger emotion words by parents and children. CONCLUSIONS: Results suggest that objective linguistic correlates of CU traits are detectable during early childhood, which could inform adjunctive treatment modules that improve outcomes by precisely tracking and targeting subtle communication patterns.
Authors: R. Waller, M. Flum, Y. Paz, E. R. Perkins, Y. Rodriguez, A. Knox, M.R. Pelella, C. Jones, S. Sun, S.A. Denham, J. Herrington, J. Parish-Morris
Language: English
Subfield: Clinical Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: hybrid
Estimated APC paid: $2,890
Funders: Michigan Department of Mental Health, University of Pennsylvania, Hebrew University of Jerusalem, Israel Science Foundation, National Institutes of Health, National Institute of Mental Health, National Institute on Deafness and Other Communication Disorders, National Center for Advancing Translational Sciences
Awards: Digitizing Human Vocal Interaction to Understand and Diagnose Autism, G8045230006, Optimized Affective Computing Measures of Social Processes and Negative Valence in Youth Psychopathology, Ethical and Human Factors Impacting Successful Translation of Perceptual Computing to Improve Clinical Care, G930878211
OpenAlex ID: W4399685179