Nadja R. Ging-Jehli
Department of Cognitive, Linguistic & Psychological Sciences
Brown University
Beyond mechanistic models: Leveraging physiological measures to dissect learning and social cooperation processes in mood and neurodevelopmental disorders:
In the digital age, where large-scale data collection is increasingly feasible, computational psychiatry faces a key question: can we rely solely on online studies with calibrated tasks that focus on behavior, or do we still need in-person studies that integrate physiological measures? Across two studies, I will demonstrate that combining mechanistic assessments with physiological measures provides more insights than behavioral analyses alone. In the first study, we employed an instrumental learning task to investigate learning impairments in major depressive disorder (MDD) and bipolar disorder (BP). The mechanistic task allowed us to distinguish between processes linked to reinforcement learning and working memory. Despite similar behavioral deficits across clinical groups, model-based EEG analyses revealed distinct neurocomputational profiles specific to each disorder. In the second study, we investigated how adults with attention-deficit/hyperactivity disorder (ADHD) respond to contextual changes during a social strategic interaction game, using eye-tracking and diffusion decision modeling to analyze their behavior. Although adults with ADHD engaged in riskier actions when stakes were high, model-based eye-tracking revealed that this was not due to impulsivity. Instead, they focused more on the potential benefits than the costs of collaboration. Further analysis showed that individuals with higher ADHD symptom severity were more sensitive to contextual changes, highlighting the dynamic nature of impulsivity.These findings emphasize the value of neurocomputational assessments, incorporating physiological measures, in distinguishing psychiatric disorders and capturing reactivity to contextual change as a potentially important transdiagnostic marker.
