A Cognitive Load-theoretic Framework for Information Visualization
L. Perkhofer - A Cognitive Load-theoretic Framework for Information Visualization - Proceedings of the 17th Finance, Risk and Accounting Perspectives Conference (FRAP), Helsinki, Finnland, 2019, pp. 9-25
This paper introduces cognitive load theory as a possible framework for information visualization in management accounting to enhance usability (effectiveness, efficiency, and satisfaction) by evaluating and improving design. Visualization is increasingly gaining importance as data samples are increasing in complexity because they enable decision-makers to extract larger amounts of information in a fast and easy manner. As opposed to traditional frameworks, which primarily focus on technical steps to transform raw data into the final layout, also called visualization, the cognitive perspective stresses the importance of humans and their cognitive capabilities to extract information from a particular and presented visualization. More precisely, it emphasizes the process of encoding data via the management accountant as well as decoding views via the respective decision-maker and the individual differences that may distort or alter interpretation. Cognitive load theory can explain a significant number of phenomena, including the mechanisms behind the frequently found problems with the adoption of new visualization options amongst accounting professionals as well as the striking influence of experience, which is responsible for a lot of contradicting results on visualization choice depending on the sample population or user group. Further, the framework contributes to the ongoing discussion on suitable evaluation methods. The proposed framework enables the information visualization community, which is responsible for the development of new visualization types, to evaluate and compare alternative visualization options outside of the traditional small-scale user studies or expert feedback; they can then shift the focus towards a more quantitative assessment based on both experimental research and an analysis within their area of application.