Decision making can be deﬁned as the process of choosing a preferred option or course of action from a set of alternatives. In order to understand how humans make decisions and even predict decision-making behavior, academics in various fields such as psychology and economics, have developed formal models of decision-making behavior. These models have been inspired by behavioral data and the computational demands required of a decision making task. For example, sequential sampling models have been developed to account for behavioral data, and reinforcement learning models for repeated choice tasks have been developed to account for the computational demands of a task.
Importantly, cognitive functions such as decision making, cannot be completely understood on the basis of mathematical models and behavioral data alone; consideration of the neuronal processes underlying decision making is crucial. Thus, we are currently investigating how mental (cognitive) and neuronal processes map onto each other. A central goal of our group is to explicitly link brain function and behavior using formal models of decision-making behavior.
In pursuit of this goal, we’re investigating decision making in three different domains:
In order to conduct the above research we employ an integrative, multimodal and methodological approach consisting of techniques ranging from cognitive modeling based on behavioral data to functional magnetic resonance imaging (fMRI) and magnetencephalographic (MEG) experiments.