By cognitive adaptive interaction systems, we mean technical system which are capable of recognizing the state of its user and to adapt optimally to this state. The user state comprises cognitive and affective aspects of the user, e.g. the degree of cognitive workload, the focus of attention or memory configuration. On the one hand, a user state can be recognized using empirical cognitive modeling, i.e. by automatically processing and classifying biosignals (e.g. EEG for the recording of brain activity). On the other hand, we can use computational cognitive models to predict user states, i.e. from executable descriptions of cognitive processes (e.g. working memory). Empirical and computational methods can be combined with each other to enable more complex and more robust predictions. Finally, a cognitive adaptive interaction system also comprises an adaptive interaction manager, which automatically adjusts the behavior of the system to the detected user state.
Putze, Felix, Maximilian Scherer, and Tanja Schultz. 2016. "Starring into the Void? Classifying Internal vs. External Attention from EEG." In Proceedings of 9th Nordic Conference on Human-Computer Interaction (NordiCHI). Gothenborg, Sweden.
Putze, Felix, and Tanja Schultz. 2014. "Investigating Intrusiveness of Workload Adaptation." In Proceedings of International Conference on Multimodal Interfaces. Istanbul, Turkey.
Heger, Dominic, Felix Putze, and Tanja Schultz. 2011. "An EEG Adaptive Information System for an Empathic Robot." International Journal of Social Robotics 3 (4): 415–25.
Ansprechpartner: Dr. Felix Putze