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Research Domains

Predictive Modeling

Nicolas Riesterer & Daniel Brand

Cognitive Science and Computer Science have tackled computational modeling from different perspectives. While cognitive science focuses on maximizing the information gained from modeling endeavors, computer science prioritizes performance in applications. In a predictive modeling setting, we aim to draw computer science and cognitive science closer together. We apply techniques from statistical modeling, information and database systems, as well as machine learning to contribute to the interdisciplinary domain of human reasoning.

Bayesian Modeling

Lukas Elflein

In order to understand cognition, we want to learn about unobserved cognitive processes. These can be inferred by proposing models and fitting parameters to experimental data. Bayesian statistics not only finds best-fit values for these parameters, but also describes our uncertainty about the inference process. This opens up new avenues for analysing existing models as well as predicting human behavior in a way that is still well interpretable.



Barbara Kuhnert

Core of our research in the field of Human-Robot Interaction is the human being and human’s perception, attitudes, concerns and emotions towards robots. Our objective is to gain a better understanding of how humans will interact with robots and the identification of essential features and characteristics, which can increase the acceptance of robots, reduce prejudices, enhance a social relationship between human and robot and thus support a better collaboration between human and robot.

Neuroscientific Modeling

Julia Wertheim