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Using High-Frequency Interaction Events to Automatically Classify Cognitive Load

Using High-Frequency Interaction Events to Automatically Classify Cognitive Load

Tao Lin, Zhiming Wu, Yu Chen
Copyright: © 2017 |Pages: 18
ISBN13: 9781522517597|ISBN10: 1522517596|EISBN13: 9781522517603
DOI: 10.4018/978-1-5225-1759-7.ch119
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MLA

Lin, Tao, et al. "Using High-Frequency Interaction Events to Automatically Classify Cognitive Load." Artificial Intelligence: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2017, pp. 2864-2881. https://doi.org/10.4018/978-1-5225-1759-7.ch119

APA

Lin, T., Wu, Z., & Chen, Y. (2017). Using High-Frequency Interaction Events to Automatically Classify Cognitive Load. In I. Management Association (Ed.), Artificial Intelligence: Concepts, Methodologies, Tools, and Applications (pp. 2864-2881). IGI Global. https://doi.org/10.4018/978-1-5225-1759-7.ch119

Chicago

Lin, Tao, Zhiming Wu, and Yu Chen. "Using High-Frequency Interaction Events to Automatically Classify Cognitive Load." In Artificial Intelligence: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 2864-2881. Hershey, PA: IGI Global, 2017. https://doi.org/10.4018/978-1-5225-1759-7.ch119

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Abstract

There is still a challenge of creating an evaluation method which can not only unobtrusively collect data without supplement equipment but also objectively, quantitatively and real-time evaluate cognitive load of users based the data. The study explores the possibility of using the features extracted from high-frequency interaction (HFI) events to evaluate cognitive load to respond the challenge. Specifically, back-propagation neural networks, along with two feature selection methods (nBset and SFS), were used as the classifier and it was able to use a set of features to differentiate three cognitive load levels with an accuracy of 74.27%. The main contributions of the research are: (1) knowledge about what detailed features may be predictive of cognitive load changes; (2) demonstrating the potential of using the HFI features in discriminating different cognitive load when suitable classifier and features are adopted.

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