Supporting Cognition in the Face of Political Data and Discourse: A Mental Models Perspective on Designing Information Visualization Systems

Background Heuristic evaluation is used in human–computer interaction studies to assess the usability of information systems. Nielsen's widely used heuristics, first developed in 1990, are appropriate for general usability but do not specifically address usability in systems that produce information visualizations. Objective This article develops a heuristic evaluation checklist that can be used to evaluate systems that produce information visualizations. Principles from Nielsen's heuristics were combined with heuristic principles developed by prior researchers specifically to evaluate information visualization. Methods We used nominal group technique to determine an appropriate final set. The combined existing usability principles and associated factors were distributed via email to a group of 12 informatics experts from a range of health care disciplines. Respondents were asked to rate each factor on its importance as an evaluation heuristic for visualization systems on a scale from 1 (definitely don't include) to 10 (definitely include). The distribution of scores for each item were calculated. A median score of ≥8 represented consensus for inclusion in the final checklist. Results Ten of 12 experts responded with rankings and written comments. The final checklist consists of 10 usability principles (7 general and 3 specific to information visualization) substantiated by 49 usability factors. Three nursing informatics experts then used the checklist to evaluate a vital sign dashboard developed for home care nurses, using a task list designed to explore the full functionality of the dashboard. The experts used the checklist without difficulty, and indicated that it covered all major usability problems encountered during task completion. Conclusion The growing capacity to generate and electronically process health data suggests that data visualization will be increasingly important. A checklist of usability heuristics for evaluating information visualization systems can contribute to assuring high quality in electronic data systems developed for health care.

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Task-based assessment of visualization tools for the comparison of biological taxonomies

Research Ideas and Outcomes ◽

10.3897/rio.4.e25742 ◽

2018 ◽

Vol 4 ◽

pp. e25742 ◽

Cited By ~ 2

Author(s):

Lilliana Sancho-Chavarria ◽

Fabian Beck ◽

Daniel Weiskopf ◽

Erick Mata-Montero

Keyword(s):

Information Visualization ◽

Matrix Representations ◽

Complex Information ◽

Analytical Reasoning ◽

Fundamental Information ◽

Visualization Tools ◽

Similarities And Differences ◽

Visualization Systems ◽

Global Initiatives ◽

Visual Interfaces

Maintenance and curation of large-sized biological taxonomies are complex and laborious activities. Information visualization systems use interactive visual interfaces to facilitate analytical reasoning on complex information. Several approaches such as treemaps, indented lists, cone trees, radial trees, and many others have been used to visualize and analyze a single taxonomy. In addition, methods such as edge drawing, animation, and matrix representations have been used for comparing trees. Visualizing similarities and differences between two or more large taxonomies is harder than the visualization of a single taxonomy. On one hand, less space is available on the screen to display each tree; on the other hand, differences should be highlighted. The comparison of two alternative taxonomies and the analysis of a taxonomy as it evolves over time provide fundamental information to taxonomists and global initiatives that promote standardization and integration of taxonomic databases to better document biodiversity and support its conservation. In this work we assess how ten user visualization tasks for the curation of biological taxonomies are supported by several visualization tools. Tasks include the identification of conditions such as congruent taxa, splits, merges, and new species added to a taxonomy. We consider tools that have gone beyond the prototype stage, that have been described in peer-reviewed publications, or are in current use. We conclude with the identification of challenges for future development of taxonomy comparison tools.

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Effect of the Amount of Task-Relevant Information on Shared Mental Models in Computer-Mediated and Face-to-Face Teams: Is More Always Better?

Social Behavior and Personality An International Journal ◽

10.2224/sbp.2009.37.9.1153 ◽

2009 ◽

Vol 37 (9) ◽

pp. 1153-1160 ◽

Cited By ~ 4

Author(s):

Xiao-yun Xie ◽

Yan Zhu ◽

Zhong-Ming Wang

Keyword(s):

Factorial Design ◽

Mental Models ◽

Relevant Information ◽

Shared Mental Models ◽

Face To Face ◽

Amount Of Information ◽

Simulated Experiment ◽

Computer Mediated ◽

Communication Modes ◽

The Face

The effect of the amount of task-relevant information on shared mental models in computer-mediated and face-to-face settings was examined. A 3 × 2 factorial design combining the amount of information with communication modes was administered through a simulated experiment. Results showed that the effects of the amount of information on the formation of shared mental models were discrepant. In the computer-mediated setting, the sharedness of mental models increased as the amount of information increased; in the face-to-face setting, the sharedness of mental models declined as the amount of information increased. The reversed results under the two communication settings extend the shared mental models theory into more contingent facets. Theoretical interpretations and limitations are discussed.

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A Meta-Analytic Examination of the Continued Influence of Misinformation in the Face of Correction: How Powerful Is It, Why Does It Happen, and How to Stop It?

Communication Research ◽

10.1177/0093650219854600 ◽

2019 ◽

Vol 47 (2) ◽

pp. 155-177 ◽

Cited By ~ 23

Author(s):

Nathan Walter ◽

Riva Tukachinsky

Keyword(s):

Mental Models ◽

Meta Analysis ◽

Time Lag ◽

The Face ◽

Credible Source

A meta-analysis was conducted to examine the extent of continued influence of misinformation in the face of correction and the theoretical explanations of this phenomenon. Aggregation of results from 32 studies ( N = 6,527) revealed that, on average, correction does not entirely eliminate the effect of misinformation ( r = –.05, p = .045). Corrective messages were found to be more successful when they are coherent, consistent with the audience’s worldview, and delivered by the source of the misinformation itself. Corrections are less effective if the misinformation was attributed to a credible source, the misinformation has been repeated multiple times prior to correction, or when there was a time lag between the delivery of the misinformation and the correction. These findings are consistent with predictions based on theories of mental models and offer concrete recommendations for practitioners.

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