TU Berlin

Cognitive Modeling in dynamic Human-Machine SystemsCognitive Modeling in dynamic Human-Machine Systems

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Lupe

Welcome on the website of the Chair of Cognitive Modeling in dynamic Human-Machine Systems!

Our main goal consists in linking Cognitive Psychology and applied Engineering Sciences, resulting in a vibrant and pulsating field of research. Cognitive modelling provides the opportunity to investigate assumptions on cognitive processes even in complex tasks and work situations.

In this purpose, it can use already implemented cognitive architectures like ACT-R for simulating fundamental cognitive processes used for task-specific models. Moreover, simulations of technical systems may be linked to cognitive architectures, to faciliate interaction between model and simulation. In this way, predictions on prospective system handling can be derived.

We develop modeling approaches of cognitive mechanisms to improve technical systems in applied domains. For this goal we work together with cooperation partners form university and industry. Among others together with David Peebles (University of Huddersfield, England), Stefan Kopp (Universit├Ąt Bielefeld) and Jelmer Borst (Rijksuniversiteit Groningen, Niederlande) also with partners from Daimler, DLR, VW, Airbus, Strato und Roche.

Recent Publications

Preuss, K., Raddatz, L. and Russwinkel, N. (2019). An implementation of Universal Spatial Transformative Cognition in ACT-R. In Proceedings of the 17th International Conference on Cognitive Modelling.


Fuhl, W. and Castner, N. and Kuebler, T. and Lotz, A. and Rosenstiel, W. and Kasneci, E. (2019). Ferns for area of interest free scanpath classification. In ACM (Eds.), Proceedings of the 11th ACM Symposium on Eye Tracking Research & Applications.


Lotz, A. and Russwinkel, N. and Wohlfarth, E. (2019). Response Times and Gaze Behavior of Truck Drivers in Time Critical Conditional Automated Driving Take-overs. Transportation Research Part F: Traffic Psychology and Behaviour, 64, p. 532-551.


Lotz, A. and Weissenberger, S. (2019). Predicting take-over times of truck drivers in conditional autonomous driving. In Stanton, N. (Eds.), Advances in Intelligent Systems and Computing. Advances in Human Aspects of Transportation 2018, p. 329–338.


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