Abstract:
Cognitive load refers to the total amount of working memory resources a person is
currently using. Successfully detecting the cognitive load a person is experiencing
is the first important step towards applications that adapt to a user’s current load.
Provided that cognitive load is estimated correctly, a system can enhance a user’s
experience or increase its own efficiency by adapting to this detected load. Using
digital learning environments as an example to illustrate this idea, a learning
environment could tune the difficulty of presented exercises or learning material
to match the learner’s current load to not underwhelm them, but also to prevent
overload and frustration.
Physiological sensors have great promise when cognitive load estimation is concerned
as many physiological signals show distinctive signs of cognitive load. Eye
tracking is an especially promising candidate as it does not require physical contact
between sensor and user and is therefore very subtle. A major problem is the
lack of general classifiers for cognitive load as classifiers are usually specific to a
single person and do not generalize well. For adaptive interfaces based on a user’s
cognitive load to be viable, a classifier that is accurate and performs well independently
of user and specific task would be needed. In the current doctoral thesis, I
present four studies that successively build upon each other and build up towards
an eye-tracking based classifier for cognitive load that is 1) accurate, 2) robust, 3)
can generalize, and 4) can operate in real-time.
Each of the presented studies advances our approach’s capability to generalize
one step further. Along the way, different eye-tracking features are explored and
evaluated for their suitability as predictors of cognitive load and the implications
for the distinction between cognitive load and perceptual load are discussed. The
resulting method demonstrates a degree of generalization that no other approach
has achieved and combines it with low hardware requirements and high robustness
into a method that has great promise for future applications. Overall, the results presented in this thesis may serve as a foundation for the use of eye tracking
in adaptive interfaces that react to a user’s cognitive load.
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