Predicting Cognitive Load in an Emergency Simulation Based on Behavioral and Physiological Measures

The reliable estimation of cognitive load is an integral step towards real-time adaptivity of learning or gaming environments. We introduce a novel and robust machine learning method for cognitive load assessment based on behavioral and physiological measures in a combined within- and cross-participant approach. 47 participants completed different scenarios of a commercially available emergency personnel simulation game realizing several levels of difficulty based on cognitive load. Using interaction metrics, pupil dilation, eye-fixation behavior, and heart rate data, we trained individual, participant-specific forests of extremely randomized trees differentiating between low and high cognitive load. We achieved an average classification accuracy of 72%. We then apply these participant-specific classifiers in a novel way, using similarity between participants, normalization, and relative importance of individual features to successfully achieve the same level of classification accuracy in cross-participant classification. These results indicate that a combination of behavioral and physiological indicators allows for reliable prediction of cognitive load in an emergency simulation game, opening up new avenues for adaptivity and interaction.