Written exam (in case of a small number of participants: oral tests)

Cognitive models covering learning, action and perception are presented and
discussed, including descriptive, qualitative, quantitative and neural models. In
addition, parameter optimization as well as techniques to compare models and
to interpret and evaluate model parameters are introduced. All techniques are
shown in the context of concrete models of cognitive processes. Moreover, the
necessary statistical methods are introduced in a practical, application-oriented
manner.

Students know the most important principles and techniques of cognitive modeling.
They know how to model cognitive processes, mechanisms, and learning
at different levels of complexity. They can apply various cognitive models and
modeling approaches in a goal-directed manner. Moreover, they can evaluate,
compare, and contrast different modeling approaches as well as modeling
results. They are able to judge whether a model is falsifiable and they know
how to validate and interpret cognitive models. Finally, they can use statistical
methods to quantitatively compare different cognitive models.

Book: S. Lewandowsky & S. Farrell (2011). Computational Modeling in Cognition.
Additional papers and book chapters will be supplied. Introductory course knowledge about machine learning, artificial neural networks, robotics, cognitive architectures, or artificial intelligence is required.