%0 Journal Article %1 huang2009measuring %A Huang, Weidong %A Eades, Peter %A Hong, Seok-Hee %D 2009 %J Information Visualization %K evaluation userstudies visualization %N 3 %P 139-152 %R 10.1057/ivs.2009.10 %T Measuring Effectiveness of Graph Visualizations: A Cognitive Load Perspective %U https://doi.org/10.1057/ivs.2009.10 %V 8 %X Graph visualizations are typically evaluated by comparing their differences in effectiveness, measured by task performance such as response time and accuracy. Such performance-based measures have proved to be useful in their own right. There are some situations, however, where the performance measures alone may not be sensitive enough to detect differences. This limitation can be seen from the fact that the graph viewer may achieve the same level of performance by devoting different amounts of cognitive effort. In addition, it is not often that individual performance measures are consistently in favor of a particular visualization. This makes design and evaluation difficult in choosing one visualization over another. In an attempt to overcome the above-mentioned limitations, we measure the effectiveness of graph visualizations from a cognitive load perspective. Human memory as an information processing system and recent results from cognitive load research are reviewed first. The construct of cognitive load in the context of graph visualization is proposed and discussed. A model of user task performance, mental effort and cognitive load is proposed thereafter to further reveal the interacting relations between these three concepts. A cognitive load measure called mental effort is introduced and this measure is further combined with traditional performance measures into a single multi-dimensional measure called visualization efficiency. The proposed model and measurements are tested in a user study for validity. Implications of the cognitive load considerations in graph visualization are discussed.

@article{huang2009measuring, abstract = {Graph visualizations are typically evaluated by comparing their differences in effectiveness, measured by task performance such as response time and accuracy. Such performance-based measures have proved to be useful in their own right. There are some situations, however, where the performance measures alone may not be sensitive enough to detect differences. This limitation can be seen from the fact that the graph viewer may achieve the same level of performance by devoting different amounts of cognitive effort. In addition, it is not often that individual performance measures are consistently in favor of a particular visualization. This makes design and evaluation difficult in choosing one visualization over another. In an attempt to overcome the above-mentioned limitations, we measure the effectiveness of graph visualizations from a cognitive load perspective. Human memory as an information processing system and recent results from cognitive load research are reviewed first. The construct of cognitive load in the context of graph visualization is proposed and discussed. A model of user task performance, mental effort and cognitive load is proposed thereafter to further reveal the interacting relations between these three concepts. A cognitive load measure called mental effort is introduced and this measure is further combined with traditional performance measures into a single multi-dimensional measure called visualization efficiency. The proposed model and measurements are tested in a user study for validity. Implications of the cognitive load considerations in graph visualization are discussed.}, added-at = {2022-02-21T13:06:56.000+0100}, author = {Huang, Weidong and Eades, Peter and Hong, Seok-Hee}, biburl = {https://www.bibsonomy.org/bibtex/26e203810e2fb571658ae4e34a9b25b35/abernstetter}, doi = {10.1057/ivs.2009.10}, eprint = {https://doi.org/10.1057/ivs.2009.10}, interhash = {74ee573c6458d9658137abd64eba569a}, intrahash = {6e203810e2fb571658ae4e34a9b25b35}, journal = {Information Visualization}, keywords = {evaluation userstudies visualization}, number = 3, pages = {139-152}, timestamp = {2022-02-21T13:06:56.000+0100}, title = {Measuring Effectiveness of Graph Visualizations: A Cognitive Load Perspective}, url = {https://doi.org/10.1057/ivs.2009.10}, volume = 8, year = 2009 }