Assessing Individual Differences in Adult Theory of Mind Through Mental State Language: Human and Artificial Intelligence Approaches

Theory of mind is central to human social behavior, yet empirical studies rely largely on biased measures with limited ecological validity. We propose and evaluate the use of mental state language as a spontaneous indicator of theory of mind expressed in social interaction. Among research participants (334 undergraduate students, 63.8% non-Hispanic White, 74.3% female) engaged in a dyadic cooperative task with an unfamiliar peer, mental state language demonstrated strong convergent validity with established measures of theory of mind and, when compared to established measures, superior predictive validity for interpersonal functioning. Fine-tuned transformers and open-source generative large language models reliably replicated human coding of mental state language, enabling scalable assessment. These findings offer promising initial construct-validity evidence for mental state language as a measure of theory of mind, and motivate its extension to naturalistic, dynamically unfolding social interactions.