Using High-Frequency Interaction Events to Automatically Classify Cognitive Load

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Author(s): Tao Lin (Sichuan University, China), Zhiming Wu (Sichuan University, China)and Yu Chen (Sichuan University for Nationalities, China)
Copyright: 2015
Pages: 19
Source title: Human Behavior, Psychology, and Social Interaction in the Digital Era
Source Author(s)/Editor(s): Anabela Mesquita (CICE – ISCAP/Polytechnic of Porto, Portugal & Algoritmi Centre, Minho University, Portugal)and Chia-Wen Tsai (Ming Chuan University, Taiwan)
DOI: 10.4018/978-1-4666-8450-8.ch010

Keywords: Human Aspects of Technology / Information Science Reference / Social Sciences & Humanities

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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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Using High-Frequency Interaction Events to Automatically Classify Cognitive Load

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