Predicting Cognitive Load of an Individual With Knowledge Gained From Others: Improvements in Performance Using Crowdsourcing

IF 1.9 Q3 COMPUTER SCIENCE, CYBERNETICS IEEE Systems Man and Cybernetics Magazine Pub Date : 2022-01-01 DOI:10.1109/MSMC.2021.3103498
Syed Moshfeq Salaken, Imali T. Hettiarachchi, Afsana Ahmed Munia, M. Hasan, A. Khosravi, Shady M. K. Mohamed, Ashikur Rahman
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Abstract

Understanding cognitive load is important due to its inherent implications across many different disciplines. This is, in general, a difficult task due to personal nature of data normally used to infer cognitive load. In addition, an individual changes over time and his/her pattern of data changes as well, which implies past data from an individual may not reliably predict the future cognitive load of the same individual. In this article, we show that utilization of data from other people (a.k.a. crowdsourcing) offers a significant improvement in classifier performance when predicting cognitive load. We reveal that the improvement is substantial compared to an individualistic model and is statistically significant.
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用从他人那里获得的知识预测个体的认知负荷:使用众包提高绩效
理解认知负荷是很重要的,因为它在许多不同学科中具有内在的含义。一般来说,由于通常用于推断认知负荷的数据的个人性质,这是一项艰巨的任务。此外,个体随着时间的推移而变化,他/她的数据模式也在变化,这意味着个体过去的数据可能无法可靠地预测同一个体未来的认知负荷。在本文中,我们展示了利用其他人的数据(又称众包)在预测认知负荷时显著提高了分类器的性能。我们发现,与个人主义模型相比,这种改进是实质性的,并且具有统计学意义。
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来源期刊
IEEE Systems Man and Cybernetics Magazine
IEEE Systems Man and Cybernetics Magazine COMPUTER SCIENCE, CYBERNETICS-
自引率
6.20%
发文量
60
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