Measuring the task-evoked pupillary response with a remote eye tracker
Proceedings of the 2008 symposium on Eye tracking research & applications
Estimating cognitive load using remote eye tracking in a driving simulator
Proceedings of the 2010 Symposium on Eye-Tracking Research & Applications
Pupillary response based cognitive workload measurement under luminance changes
INTERACT'11 Proceedings of the 13th IFIP TC 13 international conference on Human-computer interaction - Volume Part II
Exploiting eye tracking in advanced e-learning systems
Proceedings of the 13th International Conference on Computer Systems and Technologies
Using galvanic skin response for cognitive load measurement in arithmetic and reading tasks
Proceedings of the 24th Australian Computer-Human Interaction Conference
Indexing cognitive workload based on pupillary response under luminance and emotional changes
Proceedings of the 2013 international conference on Intelligent user interfaces
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Pupillary response has been widely accepted as a physiological index of cognitive workload. It can be reliably measured with video-based eye trackers in a non-intrusive way. However, in practice commonly used measures such as pupil size or dilation might fail to evaluate cognitive workload due to various factors unrelated to workload, including luminance condition and emotional arousal. In this work, we investigate machine learning based feature extraction techniques that can both robustly index cognitive workload and adaptively handle changes of pupillary response caused by confounding factors unrelated to workload.