FEBRUARY 20, 2026
Nadja R. Ging-Jehli, PhD
Independent Investigator, Gearshift Fellowship Program (Swiss NSF Scholar)
Core Director, Adaptive Intelligence & Mental Health Mechanisms, Centre for Digital Health Interventions, University of St. Gallen, ETH Zurich, University of Zurich
Visiting Scientist, Carney Institute for Brain Science, Department of Cognitive & Psychological Science, Brown University
About the Presentation: Mental health challenges often reflect not isolated symptoms, but difficulties adapting to changing demands: knowing when to persist, when to disengage, and how to regulate emotions under uncertainty. When this adaptive balance breaks down, people may either cling rigidly to ineffective strategies or withdraw altogether, losing a sense of agency. Yet most digital mental health tools assess or intervene on single processes in isolation. This offers limited insight into the mechanisms that guide real-world behavior, making it harder to intervene at the right moment with the right support.
In this talk, I introduce a mechanism-based framework for studying and strengthening adaptability that bridges neuroscience, computational psychiatry, and game-based, naturalistic environments. I present the Gearshift Fellowship, a computationally engineered digital environment designed to explain why people become rigid, disengage, or avoid when demands change. Rather than isolating processes like effort, control, or avoidance in separate tasks, it integrates these mechanisms within one continuous interactive “supertask.” This design allows us to observe how these mechanisms interact in real time as individuals decide whether to persist or withdraw while navigating social and cognitive challenges. Using computational models to uncover the processes that guide these choices, I show how this approach reveals distinct adaptability profiles across anxiety, ADHD, and depression. Notably, we find that perceived controllability, rather than threat alone, drives disengagement, pointing to disorder-specific patterns of avoidance.
Finally, I outline ongoing work integrating generative AI agents and computational archetypes to simulate distinct adaptation strategies and test how personalized interventions might recalibrate them. Together, this framework connects mechanistic phenotyping with adaptive, CBT-informed digital interventions and real-world behavioral signals from mobile and wearable technologies. The goal is to bridge mechanism, measurement, and intervention, supporting next-generation mental health tools that learn and adapt alongside the people who use them.
About the Presenter: Nadja R. Ging-Jehli, PhD, is a computational neuroscientist and psychologist leading a research program in generative AI-based computational psychiatry. Her work examines how humans and artificial agents adapt to uncertainty and changing demands, and how breakdowns in this adaptability give rise to rigidity and avoidance across psychiatric conditions. She combines computational modeling with dynamic, naturalistic environments to assess the mechanisms linking brain, behavior, and clinical outcomes. She is the founder and principal investigator of the Gearshift Fellowship, a computational platform that captures how people adapt across cognitive, affective, and social contexts. She uses these adaptation profiles to personalize mental health interventions. Her research bridges foundational neuroscience with scalable digital mental health approaches, translating mechanistic insights into precision treatment strategies. Her work has been recognized with awards from the American College of Neuropsychopharmacology and the Society of Biological Psychiatry.