A cognitive load approach to designing and evaluating data visualizations

Visual representations of data are increasingly prevalent, but we lack a detailed theoretical framework to explain what factors make easy or difficult to read and understand; nor do we know how such factors can impact data visualizations’ efficiency as learning material.

In this Master’s Thesis, I address this gap by exploring the validity and applicability of Cognitive Load Theory, an educational and cognitive science theoretical framework, for designing and evaluating data visualizations. Beyond empirical findings, I also contribute an interdisciplinary perspective on the cognitive processing of visualizations, and I discuss implications in assessing readability in visualization studies.

This work was conducted for my Master’s Thesis under the supervision of Lorenzo Ciccione, Petra Isenberg, and Tobias Isenberg.

This figure presents the study’s stimuli contents., consisting in a series of maps showing evolution of temperatures worlwid according to 4 different climate change scenarii each. 

There are 4 parts in this figure: upper part is the content A about heat-humidity risks for humans, and the lower part in the content B related to the risk of loss species. On the left side of each part is a compact version and on the right side a segmented version.

The representations from Figure SPM3 in the IPCC report SPM Calvin et al. (2023). Textual captions accompany each visualization.
Visualizations used in the study. Each visualization content was adapted into two different presentation styles.
Each participant saw two visualizations: one of each style and content.