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Cross-task and cross-participant classification of cognitive load in an emergency simulation game

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journal contribution
posted on 23.11.2021, 14:20 by Tobias Appel, Peter Gerjets, Stefan Hoffman, Korbinian MoellerKorbinian Moeller, Manuel Ninaus, Christian Scharinger, Natalia Sevcenko, Franz Wortha, Enkelejda Kasneci
Assessment of cognitive load is a major step towards adaptive interfaces. However, non-invasive assessment is rather subjective as well as task specific and generalizes poorly, mainly due to methodological limitations. Additionally, it heavily relies on performance data like game scores or test results. In this study, we present an eye-tracking approach that circumvents these shortcomings and allows for effective generalizing across participants and tasks. First, we established classifiers for predicting cognitive load individually for a typical working memory task (n-back), which we then applied to an emergency simulation game by considering the similar ones and weighting their predictions. Standardization steps helped achieve high levels of cross-task and cross-participant classification accuracy between 63.78% and 67.25% for the distinction between easy and hard levels of the emergency simulation game. These very promising results could pave the way for novel adaptive computer-human interaction across domains and particularly for gaming and learning environments.


LEAD Graduate School and Research Network [GSC1028]



  • Science


  • Mathematics Education Centre

Published in

IEEE Transactions on Affective Computing


Institute of Electrical and Electronics Engineers


AM (Accepted Manuscript)

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Publication date









Prof Korbinian Moeller. Deposit date: 22 November 2021