Our lab investigates the neural and computational mechanisms that enable us to think about and act in our social world. We aim to discover how the brain represents social information, distinguishes self from others, and learns from social experiences. By integrating neuroimaging, computational modelling, and brain stimulation techniques, we bridge multiple levels of analysis—from neural circuits to real-world behavior—to understand the "social operating system" of the human brain and its implications for mental health and well-being.
Abstract representation in social cognition
Getting around in the social world is more than just learning about ourselves and others. It is about learning relationships between people and building flexible representations of social structures. Our research shows the brain uses fundamental "building blocks" to track complex social interactions. These abstract representations compress information about social patterns into efficient neural codes, allowing us to navigate diverse social environments without cognitive overload. One current major research direction in our lab is to investigate the abstract representations underlying our social behaviour more generally, using neuroimaging, behavioural modelling, and brain stimulation, and to understand their consequences on our everyday decisions and mental wellbeing.
Key publication: Wittmann et al., 2025, Nature
Figure credit: Wittmann, M.K. et al./Nature (CC BY 4.0)
Distinguishing self and other
As persons, we usually have a firm sense who we are – our decisions, goals, and identity. Yet we can easily switch perspective with a friend and see the world from their perspective. Sometimes, we identify with others very strongly and it is a common phenomenon to identify with one's ingroup or the football team you support. Under those conditions, people can misattribute the success of their own actions to others or the inverse way around, something that we call self-other mergence. We are studying the neural underpinnings of self-other mergence using reinforcement learning models, neuroimaging and brain stimulation. Understanding when self-other mergence impairs us versus when it enhances adaptive behavior could provide crucial insights into both healthy social cognition and its potential disruptions in various conditions.
Key publications: Wittmann et al., 2016, 2021, Neuron
(Figure from Wittmann et al., 2018, Ann Rev Neuro)
Learning and decision making
Social behaviour is particularly intriguing because of its relevance to our daily experience and the effortlessness with which we as humans seamlessly navigate complex environments. However, the mechanisms employed by our brains to achieve this flexibility are unlikely to be purely social in nature. Therefore, we are more broadly interested in the neural and computational mechanisms underlying learning and decision making. One particular focus has been how people learn from rewards, build a general sense of their current environment's value, and track how this value has changed over longer timescales. These processes help us situate our actions in a broader context. Using behavioural experiments, brain imaging, and computational modelling, we investigate the component mechanisms supporting these abilities in the prefrontal cortex.
Key publications: Wittmann et al., 2016, 2020, Nat Commun
Figure credit: Wittmann, M.K. et al./Nat Commun (CC BY 4.0)
The cognitive neuroscience toolbox
We are use different methods to understand social behaviour and its neural correlates. This includes brain imaging methods (fMRI and MEG), computational modelling, but also brain stimulation methods such as TMS and transcranial ultrasound stimulation (TUS). TUS is a new emphasis in our lab and we are currently setting up TUS as a new method at UCL. Unlike conventional non-invasive brain stimulation methods, TUS can safely and precisely stimulate deep brain regions such as medial prefrontal cortex, the amygdala and the hippocampus, which we plan to investigate in future studies.
