CSHC - Center for the Study of Human Cognition

I am a post doc at the department of Psychology at the University of Oslo. Before coming to Oslo I completed my PhD at the Max Planck Institue for Human Development in Berlin, and was a post doc at the same institute and later at the Freie Universität Berlin.

My main research interests are mechanistic models of decision making and learning. That is, I work on simple mathematical models that describe peoples behavior and the underlying cognitive and brain mechanisms as observed with fMRI and EEG. Most of the time I apply such models to describe decision making in the context of social learning and value based decisions.

I am also interested in fMRI methods, in particular in developping new methods for fMRI meta analysis and efficient denoising of fMRI data.

Research in cognitive neuroscience is often a collaborative effort, and I am happy to work together with nice and competent colleagues like Hauke Heekeren (FU Berlin) Jörg Rieskamp (Uni Basel) and Siri Leknes and Tor Endestad here at the CSHC.

Research Example: How the brain integrates costs and benefits during decision making
(Basten, Biele, Heekeren & Fiebach, PNAS 2010)

Many of our choices depend on the costs and benefits associated with different alternatives. For instance, grocery shoppers might wonder if the additional cost of brand products are justfied by their better quality. The aim of this experiment was to identify the brain mechanisms supporting cost-benefti decisision making. Participants saw a colored shape, for instance a green square or a red disk, on the screen that they had to either accept or reject. Prior to the decision making task participants had learned that different colors were associated with different loss amounts and different shapes with different gain amounts (or vise versa). Hence participants should accept a colored shape only if the associated gain was larger than the associated loss.

We used a drift diffusion model to estimate for each participant separately the drift rate, representing the efficiency of information processing during decision making. This individual difference variable was then used in the fMRI analysis to identify brain regions implementing specific sub-mechanisms of decision making. The figure illustrates our main results.

 In the center of the figure is in illustration of a drift diffusion of decision making. According to this model, accumulation of noisy information (here the difference between gain and loss) starts at a point between two decision boundaries and continues until one of the decision boundaries is passed and an “accept” (the correct choice in this example) or “reject” response is executed. Reaction time (RT) distributions are shown below and above the decision boundaries. In this model higher difficulty levels (or less efficient information processing) is associated with a slower accumulation of information.

Our combined modeling and fMRI analysis suggests that the brain implements cost benefit decisions in three steps. First, the costs and benefits associated with a decision option are represented in ventral striatum and amygdala, respectively. Next, the ventromedial prefrontal cortex computes the difference between cost and benefits. Finally, this difference signal is accumulated in the intraparietal sulcus until the decision threshold is passed.