Ricarda Schubotz (Münster)

Rethinking Brain Science - How a Dynamical Systems Approach Could Pave the Way for New Treatments and Cures
April 28, 2026, 5.30 PM, Lecture Hall 04, University Medical Center Göttingen

Despite rapid advances in neuroscience, effective treatments for brain disorders - from Alzheimer’s to depression and Parkinson’s - have proven challenging to develop. In her new book, Elusive Cures, Prof. Nicole C. Rust argues that we need to rethink our approach: Treating the brain is less like fixing a broken machine and more like guiding a hurricane - a complex, ever-shifting system that demands innovative strategies. Join us for an evening with Nicole C Rust as she shares key insights from her book and explores how neuroscience can move from discovery to real-world impact. What would it take to identify the brain’s critical control nodes? How could a coordinated, community-driven “Grand Plan” accelerate progress? And can we ever hope to steer such a dynamic system toward health? After a brief presentation, Prof. Rust will address these questions together with Svilen Georgiev, host of the Neuroscience & Beyond podcast, followed by an open conversation with the audience.

From Subjective Feelings to Brain Mechanisms: Advancing the Science of Mood through Epistemic Iteration
April 28, 2026, 2 PM, Michael Lankeit Lecture Hall, German Primate Center

What happens in our brains to generate our moods? We don't yet know. Unlike functions like memory that can be measured objectively, mood is typically assessed with subjective ratings, such as “On a scale of 1 to 5, how excited (or upset) are you?” At the same time, our most precise neural measurements come from nonhuman animals, who cannot report how they feel. Mood is an extreme example of a central problem in neuroscience: we need measurements to create understanding, but we need understanding to design good measurements. Once we fully understand mood, we'll know exactly how to measure it (for instance, in an animal). But how do we get there? In this talk, I will draw on the notion of “epistemic iteration,” proposed by the philosopher Hasok Chang, to describe how scientists tackled the conceptually analogous problem of understanding temperature in the 17th century. Building on this idea, we have developed a new approach to bridge the gap between behavioral and neural measures of mood. I will describe how we have used this strategy to identify a strong neural correlate of mood, reflected in the heterogeneous, persistent responses of individual neurons in monkey anterior insular cortex — a brain area implicated in human mood via lesions, fMRI, and microstimulation. Finally, I will describe how these insights are leading us to detailed accounts of how the brain converts experiences into mood and the mechanisms that keep mood within a healthy range, rather than spiraling out of control.

Brain mechanisms engaged in social interactions
March 16, 2026, 2 PM, Michael Lankeit Lecture Hall, German Primate Center

The brain computations engaged in social learning have mostly been investigated in dyadic interactions and in small groups. Yet, it is still unclear exactly what are the different types of computational mechanisms used by the brain for making inferences about others in different types of social interactions. First, I will present a taxonomy of distinct computations used by the brain for learning and inferences made during social interactions (reward learning, belief learning, mental-state learning and distributed learning). I will illustrate this taxonomy with examples from experiments performed in our lab. In particular, I will present recent model-based functional MRI results concerning mental-state learning in humans, children and baboons. In human adults, I will show how the brain adapts to fluctuating intentions of others when the nature of the interactions (to cooperate or compete) is not explicitly and truthfully signaled. In Guinea baboons living in a social colony, I will present recent data addressing whether the ability to mentalise is confined to humans. These baboons freely came to play a 2-players coordination game with any other baboon, or alone. Moreover, I will present recent models (RL vs DeGroot) and experimental work shedding light on the computations people use to integrate and transmit information in social networks. Finally, I will present a fMRI study investigating how the brain decides whether to share extra information with others, depending upon one’s own confidence about the reliability of information and upon one’s beliefs concerning the preferences of receivers. Together, these results pave the way towards developing a mechanistic understanding of social learning mechanisms used by networked individuals interacting dynamically in real time, that shape collective performance and consensus.