Distracted in a Demanding Task: A Classification Study with Artificial Neural Networks

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An important issue in cognitive science research is to know what your subjects are thinking about. In this paper, we trained multiple artificial Neural Network (ANN) classifiers to predict whether subjects’ thoughts were focused on the task (i.e., on-task) or if they were distracted (i.e., distracted thought), based on recorded eye-tracking features and task performance. Novel in this study is that we used data from a demanding spatial complex working
memory task. The results of this study showed that we could classify on-task vs. distracted thought with an average of 60% accuracy. Task performance was found to be the strongest predictor of distracted thought. Eye-tracking features (e.g., pupil size, blink duration, fixation duration) were found to be much less predictive. Recent literature showed potential for eye-tracking features, but this study suggests that the nature of the task can greatly affect this potential. Rehearsal effort based on eye-movement behavior was found to be the most promising eye-tracking feature. Although speculative, we argue that eye-movement
features are independent of the content of distracted thought and may therefore
provide a more generic feature for classifying distracted thought.
Original languageEnglish
Title of host publicationProceedings of BNAIC 2017
EditorsBart Verheij, Marco Wiering
PublisherUniversity of Groningen
Number of pages14
ISBN (Print)78-94-034-0299-4
Publication statusPublished - Nov-2017
EventThe 29th Benelux Conference on Artificial Intelligence - Groningen, Netherlands
Duration: 8-Nov-20179-Nov-2017


ConferenceThe 29th Benelux Conference on Artificial Intelligence
Abbreviated titleBNAIC 2017
Internet address

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