Literature DB >> 32678781

Evaluating effectiveness of information visualizations using cognitive fit theory: A neuroergonomics approach.

Joseph K Nuamah1, Younho Seong2, Steven Jiang3, Eui Park4, Daniel Mountjoy5.   

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.
Copyright © 2020 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Cognitive fit theory; Electroencephalography; Visualization

Mesh:

Year:  2020        PMID: 32678781     DOI: 10.1016/j.apergo.2020.103173

Source DB:  PubMed          Journal:  Appl Ergon        ISSN: 0003-6870            Impact factor:   3.661


  2 in total

1.  Designing for Confidence: The Impact of Visualizing Artificial Intelligence Decisions.

Authors:  Alexander John Karran; Théophile Demazure; Antoine Hudon; Sylvain Senecal; Pierre-Majorique Léger
Journal:  Front Neurosci       Date:  2022-06-24       Impact factor: 5.152

Review 2.  A Review of EEG and fMRI Measuring Aesthetic Processing in Visual User Experience Research.

Authors:  Zhepeng Rui; Zhenyu Gu
Journal:  Comput Intell Neurosci       Date:  2021-12-16
  2 in total

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