A natural language processing approach reveals first-person pronoun usage and non-fluency as markers of therapeutic alliance in psychotherapy

• Published in: iScience Vol. 26; no. 6; p. 106860
• Main Authors: Ryu, Jihan, Heisig, Stephen, McLaughlin, Caroline, Katz, Michael, Mayberg, Helen S., Gu, Xiaosi
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 16.06.2023; Elsevier
• Subjects: Machine learning; Psychiatry; Psychiatry; Machine learning
• ISSN: 2589-0042; 2589-0042
• DOI: 10.1016/j.isci.2023.106860

It remains elusive what language markers derived from psychotherapy sessions are indicative of therapeutic alliance, limiting our capacity to assess and provide feedback on the trusting quality of the patient-clinician relationship. To address this critical knowledge gap, we leveraged feature extraction methods from natural language processing (NLP), a subfield of artificial intelligence, to quantify pronoun and non-fluency language markers that are relevant for communicative and emotional aspects of therapeutic relationships. From twenty-eight transcripts of non-manualized psychotherapy sessions recorded in outpatient clinics, we identified therapists’ first-person pronoun usage frequency and patients’ speech transition marking relaxed interaction style as potential metrics of alliance. Behavioral data from patients who played an economic game that measures social exchange (i.e. trust game) suggested that therapists’ first-person pronoun usage may influence alliance ratings through their diminished trusting behavior toward therapists. Together, this work supports that communicative language features in patient-therapist dialogues could be markers of alliance. [Display omitted] •Usage of “i” and “we” in therapist speech characterizes sessions with low alliance•Non-fluency in patient speech characterizes sessions with high alliance•Diminished trusting behavior mediates the potential impact of therapists’ “i” usage Machine learning; Psychiatry