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Jena - Pasadena

German - US-American Collaboration in Computational Neuroscience

"Computational and Neural Mechanisms of Inference over Decision-Structure"

How do we make decisions? Typical decision-making experiments look at how human participants make decisions in the presence of several alternatives to choose from. Usually, participants know (by instruction) or learn quickly what these alternatives are, e.g. whether they should draw a card from the left or right deck of cards to optimize their winning chances. However, since most studies focus on the decision making process itself, it is unclear how the brain can learn the so-called structure of a given decision problem so quickly and accurately.

To address this question, we will develop a variety of computational models that deploy different strategies of how humans may learn the structure of decision making problems. We will compare these models and combine them with behavioral and functional magnetic resonance imaging data from human participants. This enables us to address which computational model can best explain how humans learn the structure of decision making problems.

We expect that this project will lead to a novel and in-depth understanding of the contribution of different brain regions to decision-making. It will also provide insight into the computational mechanisms and neural implementation of a fundamental part of human decision-making: how we identify the structure of decision problems.


The following scientists take part in this collaboration:

  • Stefan Kiebel, Friedrich-Schiller-University Jena
  • John O’Doherty and Peter Bossaerts, California Institute of Technology, Pasadena, USA


German Coordinator:

Prof. Dr. Stefan Kiebel
Professur für Neuroimaging
Fachrichtung Psychologie
Fakultät Mathematik und Naturwissenschaften
Technische Universität Dresden
01062 Dresden
beforer: Friedrich-Schiller-Universität Jena