Abstract
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.
Highlights
I N many digital environments designed for learning, working or even for entertainment purposes there is a close link between users’ current cognitive load and their affective experiences
Since cognitive load can be interpreted as a form of arousal - as illustrated by its impact on pupil diameter - managing it would be beneficial in many situations
We show how a machine learning approach might be used for cognitive load detection based on eye-tracking data to allow for successful generalization across participants and tasks
Summary
I N many digital environments designed for learning, working or even for entertainment purposes there is a close link between users’ current cognitive load and their affective experiences. Similar to the zone of Tobias Appel was with LEAD Graduate School and Research Network, Tubingen, Germany. He is with the Department of Human-Computer Interaction, University of Tubingen, Tubingen , Germany. Korbinian Moeller was with the Leibniz-Institut fur Wissensmedien, Tubingen He is with the Centre for Mathematical Cognition, University of Loughborough, UK. Franz Wortha was with LEAD Graduate School and Research Network, Tubingen, Germany He is with the Leibniz-Institut fur Wissensmedien, Tubingen, Germany. For calibrating learning experiences with regard to their cognitive demands and affective connotations technical support systems might be helpful Such a system could optimize the resulting learning outcomes by performing real-time adjustments of the cognitiveload level imposed by a learning environment
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