2019 - now: Postdoc with Prof. Christian C. Ruff (University of Zurich)

I used neural encoding models of numerical representations for fMRI to characterize individual differences in numerical acuity and risk aversion

2017 - 2019: Postdoc with Dr. Tomas Knapen (Vrije Universiteit)

I mapped out ocular dominance columns and retinotopy across cortical depth, using 0.7mm isotropic functional MRI.


2013 - 2018: PhD with Prof. Birte U. Forstmann (University of Amsterdam)

I studied subdivisions in the subthalamic nucleus, a subcortical node of the basal ganglia. I used 7 Tesla MRI and computational models like the drift diffusion model (DDM)

For my PhD thesis, I was awarded the 2018-2019 dissertation award from The Dutch Society for Brain and Cognition.

Download thesis

2010 - 2012: MSc in Artificial Intelligence

Focus on machine learning and computer vision


2018: NWO Rubicon grant

To join the Zurich center for Neuroeconomics and do research on the role of parietal numerosity representations in risky decision-making.

1 Jul 2019 - 1 Sep 2021

EUR 158,000

2021: UZH Forschungskredit

To study influence of TMS on parietal number representations during risky choice

1 Dec 2021 - 1 Dec 2022

CHF 110,384

Open-source software

Research aims

I have a degree in Artificial Intelligence and did my PhD in the field of ultra-high field (UHF) MRI at field strengths of 7 Tesla and more with Prof. Birte Forstmann at the University of Amsterdam. I am convinced that UHF-fMRI has the potential to transform the field of humans neuroimaging, because the SNR is simply so much higher than at lower field strengths, allowing us to analyse brain data in individual space and at the mesoscopic resolution of subcortical nuclei, topographical maps, cortical columns, and cortical layers.

During my first postdoc I got very excited about the experimental rigor and vast knowledge in visual neuroscience and the power of encoding models that quantitatively model the relationship between objective properties of the world (e.g., the number of stimuli in a stimulus array) and neural activity. By inverting such models, we can track which information the brain has access to from moment-to-moment. I think this is a very useful measure to learn more about how the brain works.

At the ZNE, I work on the question of how the quirks of simple, everyday (economic) decisions are rooted in fundamental brain properties. To me, a central and fundamental property of the brain is that is uses its costly representational capacity only where it is most needed. This has profound consequences for economic choice behavior. For example:

  • People have trouble understanding the objective magnitude of large numbers: They tend to increasingly underestimate increasingly larger numbers. This has important consequences for how they make decisions where different numerical magnitudes play a role.
  • Neural representations of the outside world are very noisy. Thus, People need to take into account this noise when they make decisions about the outside world.
  • People can meaningfully attend only one thing at at time. Thus, what people attend at a given moment in time has a huge influence on how they make decisions. Analogously, neural representations of the outside world are to a large extent shaped by attention as well.

In my research, I try to formalise and empirically test such hypotheses using experiments involving human subjects and whole-brain UHF-fMRI measurements.

Contact information

Gilles de Hollander

University of Zurich

Department of Economics

Bl├╝mlisalpstrasse 10, Room BLU-208

8006 Zurich