Cognitive Modeling

Welcome to our Neuro-Cognitive Modeling Group

Research Statement

Our Neuro-Cognitive Modeling Group addresses the question: "How does the mind work?", pursuing an integrative, interdisciplinary, functional and computational approach.

Our main premise is that our brain is an inference system, which dynamically learns and develops event-predictive, probabilistic structures, while actively inferring – and thus generating and controlling – current attention, thoughts, and behaviors. It builds those structures in order to behave flexibly socially and adaptively in our highly complex socio-cultural environments.

To corroborate evidence and shed light on the details behind, we conduct behavioral studies in the real world as well as in virtual realities, including language production and interpretation studies.

Moreover, we are building artificial, Bayesian and deep – typically recurrent – generative neural network models to probe our integrative theoretical assumptions and to develop useful, truly artificially intelligent systems.

Meanwhile, we advance computational theories of machine learning that yield dynamically unfolding developmental, learning, and decision making processes (concurrently) and augment them with inductive learning biases that focus learning progress on critical environmental structures and causal interactions between them.

We integrate our research into further reaching aspects concerning human and artificial intelligence, including the (reflexive and reflective) self, action decision making, planning, reasoning, social interaction, forecasting systems, and language.

Further details on our research


Spatiotemproal Forecasting with DISTANA

Watch our novel DIstributed Spatio-Temporal generative Artificial Neural Architecture being explained by Matthias.



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A note on the previous "COBOSLAB":

The chair of Cognitive Modeling was previously called the COBOSLAB: COgnitive BOdy Spaces: Learning And Behavior.

The COBOSLAB has been developing artificial self-organized cognitive systems that learn multimodal modular sensorimotor bodyspace representations for effective learning and behavior. We have developed and implemented artificial adaptive systems that learn, develop, and behave autonomously based on learning principles derived from cognitive psychology and neuroscience. Meanwhile, we have investigated behavioral flexibilities and spatial representations and perceptions to (1) verify or evaluate the developed computational models and to (2) gain further insights on how space is perceived and behavior is controlled.

The funding for this project came from the Emmy Noether program of the German Research Foundation (DFG).