I’m a cognitive neuroscientist and Staff Scientist in Psychological & Brain Sciences at Washington University in St. Louis, working with Wouter Kool.

My research is about cognitive control: how we allocate attention, monitor our performance, and decide how much effort a task is worth. I think of control as something that emerges from distributed brain dynamics rather than from a single “central agent”. I try to explain it by linking computational models to their neural implementation, using behavioral experiments, EEG, and fMRI.

Before St. Louis I was a postdoc at the University of Göttingen. I did my PhD at Ghent University, with a research stay at the Donders Institute in Nijmegen. I studied psychology and neuroscience in Brescia and Padova.

Research interests

How do we deal with many sources of information? Most attention experiments have one target and one distractor. Real environments have many. I built a new paradigm (the MULTI task) to study how people handle several distractors at once. We found that attention adapts mainly by adjusting the distractors, not by ‘focusing harder’ on the task. This adaptation is specific to each distractor’s history.

How does the brain check its own work? After errors, negative feedback or surprises, the medial frontal cortex produces fast signals (feedback- and error-related negativities, theta oscillations). I use EEG to study these signals and how they feed into adaptation on the next trial.

Is it worth the effort? Control is costly, and we weigh its expected benefits against the cost of exertion. My EEG work showed that avoiding an anticipated effort produces a reward signal, and that people differ in how relieving that is.

Reading thoughts in brain waves With decoding methods I track, millisecond by millisecond, what the brain is representing. That includes the distractions it is trying to push away.

Neural geometry of conflict I am testing whether conflict and control are encoded as multivariate representations in the dACC and PFC, and whether adaptation changes their geometry.

Computational models and individual differences I build connectionist and evidence accumulation models of control, and fit them to behavior to measure individual differences. I want to use this to study adhd and depression.

When motivation runs low In depression, for example, learning can be intact while drive is not. I am interested in how motivation and control break down, and in how to measure that.

Recent highlights

• 2025: Distractor-specific control adaptation in multidimensional environments is out in Nature Human Behaviour.
• 2025: New preprint: Reactive suppression of distractor representations resolves multidimensional interference.
• 2024–2026: PI on a McDonnell Center for Systems Neuroscience grant on the neural representation of multidimensional conflict (EEG + fMRI).