Evaluating effectiveness of information visualizations using cognitive fit theory: A neuroergonomics approach. (October 2020)

• Record Type: Journal Article
• Authors: Nuamah, Joseph K.; Seong, Younho; Jiang, Steven; Park, Eui; Mountjoy, Daniel
• Is Part Of: Applied ergonomics. Volume 88(2020)
• Publication Date: 2020-10
• Subjects: Cognitive fit theory -- Visualization -- Electroencephalography; Human engineering -- Periodicals; 620.82
• DOI: 10.1016/j.apergo.2020.103173
• Languages: English
• ISSNs: 0003-6870

Abstract

Abstract: Information visualizations may be evaluated from the perspective of how they match tasks that must be performed with them, a cognitive fit perspective. However, there is a gap between the high-level references made to cognitive fit and the low-level ability to identify and measure it during human interaction with visualizations. We bridge this gap by using an electroencephalography metric derived from frontal midline theta power and parietal alpha power, known as the task load index, to determine if cognitive effort measured at the level of cortical activity is less when cognitive fit is present compared to when cognitive fit is not. We found that when there is cognitive fit between the type of problem to be solved and the information displayed by a system, the task load index is lower compared to when cognitive fit is not present. We support this finding with subjective (NASA task load index) and performance (response time and accuracy) measures. Our approach, using electroencephalography, provides supplemental information to self-report and performance measures. Findings from this study are important because they (1) provide more validity to the cognitive fit theory using a neurophysiological measure, and (2) use the electroencephalography task load index metric as a means to assess cognitive workload and effort in general. Highlights: Cognitive fit between task presentation and task type leads to better decision-making performance. Neuroergonomics holds promise for evaluating information visualizations. Electroencephalography Task Load Index is sensitive to task demand changes elicited by different visualizations. Electroencephalography Task Load Index can complement traditional information visualization evaluation methods.