The DOI matches its Crossref record; 114 of the 114 references it cites were found in a registry or an index.
Decision making with visualizations: a cognitive framework across disciplines
Open accessAbstract
Visualizations-visual representations of information, depicted in graphics-are studied by researchers in numerous ways, ranging from the study of the basic principles of creating visualizations, to the cognitive processes underlying their use, as well as how visualizations communicate complex information (such as in medical risk or spatial patterns). However, findings from different domains are rarely shared across domains though there may be domain-general principles underlying visualizations and their use. The limited cross-domain communication may be due to a lack of a unifying cognitive framework. This review aims to address this gap by proposing an integrative model that is grounded in models of visualization comprehension and a dual-process account of decision making. We review empirical studies of decision making with static two-dimensional visualizations motivated by a wide range of research goals and find significant direct and indirect support for a dual-process account of decision making with visualizations. Consistent with a dual-process model, the first type of visualization decision mechanism produces fast, easy, and computationally light decisions with visualizations. The second facilitates slower, more contemplative, and effortful decisions with visualizations. We illustrate the utility of a dual-process account of decision making with visualizations using four cross-domain findings that may constitute universal visualization principles. Further, we offer guidance for future research, including novel areas of exploration and practical recommendations for visualization designers based on cognitive theory and empirical findings.
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Zhai, C., Wibowo, S., & Li, L. D. (2024) · Smart Learning Environments · doi:10.1186/s40561-024-00316-7 · Cited by 1,567
FairSight: Visual Analytics for Fairness in Decision Making
Ahn, Y., & Lin, Y.-R. (2019) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2019.2934262 · Cited by 126
A Bayesian Cognition Approach to Improve Data Visualization
Kim, Y.-S., Walls, L. A., Krafft, P., & Hullman, J. (2019) · Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300912 · Cited by 76
A Critical Reflection on Visualization Research: Where Do Decision Making Tasks Hide?
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Metadata
- Authors
- Padilla, L. M., Creem-Regehr, S. H., Hegarty, M., & Stefanucci, J. K.
- Venue
- Cognitive Research: Principles and Implications
- Published
- 2018-07-11
- Volume
- 3
- Issue
- 1
- Pages
- 29-29
- ISSN
- 2365-7464 (ISSN-L)
- OpenAlex
- W2810874430
- Cited by
- 336
- References
- 114
- FWCI
- 13.21
- Type
- journal article
- Language
- en
- Open access
- gold
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