Using Eye Tracking to Distinguish Between Different Levels of Cognitive Workload
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2011-08-29
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en
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Abstract
Augmented cognition is a relatively new field in Human-Computer Interaction that aims for the development of systems that can detect the user’s cognitive state in real-time and consequently adapt the system appropriately. Such an adaptation could improve the effectiveness of human-computer interfaces. In this thesis a method is developed to distinguish between multiple levels of cognitive workload using eye-behavior features.
These features are based on features that are known to be distinctive when used to distinguish between other cognitive states [1]. The data are obtained using an eye tracker.
The results indicate that this method is task dependent to a degree, but these results are not conclusive. On the other hand, it is shown that this method can distinguish between different levels of cognitive workload within a task and that this measure
is subject-independent.
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Faculteit der Sociale Wetenschappen