MENTAL STATE PROCESSING &
PSYCHOPATHOLOGY LAB
Department of Psychology, Virginia Tech
Delineate how individual differences in mental state processing contributes to psychopathology
Over the past two decades, researchers have identified social and metacognitive deficits as an important mechanism underlying mental illness (e.g., National Institute of Mental Health, 2024). Individuals with various forms of mental illness are less accurate in their identification of mental states relative to those without mental illness. Further, more severe mental state processing impairments are associated with worse symptom severity and poorer interpersonal functioning. Yet, how these individual differences in invisible cognitive processes leads to real-world impairment is poorly understood and limits our ability to intervene. Our lab is interested in studying the mechanisms that link social and metacognition to real-world outcomes. To date, we have identified several behavioral mechanisms (e.g., Hudson et al., 2018; Hudson et al., 2024) and in a collaboration with Dr. Adrienne Romer, we are currently studying possible neural mechanisms.
Model the structure of mental state processing skills
Research on social and metacognition has proliferated over the last two decades in several fields, including psychiatry, clinical psychology, cognitive science, education, human development, and social psychology. Unfortunately, each of these fields has studied these constructs in silos, resulting in compartmentalized knowledge and disparate terminology. The distinctions between these related constructs are not well defined, leading to a research literature where: (1) tasks that purportedly measure the same construct are uncorrelated (Yeung et al. 2024) and (2) tasks that purportedly measure different constructs are highly correlated (e.g., Kittel et al., 2022). An overarching goal of our research is to develop and disseminate a data-driven approach to determine the underlying structure of mental state processing skills and how these skills are distributed within and across individuals.
Track how mental state processing and psychopathology progress over time and during treatment
Mental state processing skills are largely considered a trait-like characteristic that is relatively stable over time. We question this assumption and seek to empirically investigate the temporal variation in mental state processing over time. We are particularly interested in fluctuation in skills and propensity within the course of episodic mental health conditions and in the context of psychotherapeutic treatments. In our current work, we are examining how cognitive behavioral therapy impacts mental state processing skills in both outpatient and partial hospitalization settings.
Establish measures that produce reliable and valid assessments of individual differences in mental state processing skills
Assessment of mental state processing is challenging. To be ecologically valid, it requires stimuli that is contextually rich and representative of the complex social situations we encounter every day. However, these rich stimuli can quickly become outdated and come at the cost of experimental control. As such, it is perhaps not surprising that psychometric properties of popular mental state processing tasks are often poor (Yeung et al., 2024). Our research seeks to evaluate and disseminate novel methods of assessing mental state processing. In our past research, we have improved upon existing methodology (e.g., Hudson et al., 2020). In our current work, we are using novel machine learning techniques to improve the reliability, validity, and accessibility of existing assessment methods.