Journal article
Comparing Uncertainty Visualizations for a Dynamic Decision-Making Task
Journal of cognitive engineering and decision making, Vol.5(3), pp.277-293
09/2011
DOI: 10.1177/1555343411415793
Abstract
Supporting complex decision making requires conveying relevant information characteristics or qualifiers. The authors tested transparency and numeric annotation for displaying uncertainty about object identity. Participants performed a “missile defense” game in which they decided whether to destroy moving objects (which were either threatening missiles or nonthreatening birds and planes) before they reached a city. Participants were provided with uncertain information about the objects’ classifica-tions. Uncertainty was represented through the transparency of icons representing the objects and/or with numeric annotations. Three display methods were created. Icons represented the most likely object classification (with solid icons), the most likely object classification (with icons whose transparency represented the level of uncertainty), or the probability that the icon was a missile (with transparency). In a fourth condition, participants could choose among the representations. Icons either were or were not annotated with numeric probability labels. Task performance was highest when participants could toggle the displays, with little effect of numeric annotation. In conditions in which probabilities were available graphically or numerically, participants chose to engage objects when they were farther from the city and had a lower probability of being a missile. Results provided continued support for the use of graphical uncertainty representations, even when numeric representations are present.
Details
- Title: Subtitle
- Comparing Uncertainty Visualizations for a Dynamic Decision-Making Task
- Creators
- Ann M Bisantz - University at Buffalo, State University of New YorkDapeng Cao - University at Buffalo, State University of New YorkMichael Jenkins - University at Buffalo, State University of New YorkPriyadarshini R Pennathur - University at Buffalo, State University of New YorkMichael Farry - Charles River AnalyticsEmilie RothScott S Potter - Charles River AnalyticsJonathan Pfautz - Charles River Analytics
- Resource Type
- Journal article
- Publication Details
- Journal of cognitive engineering and decision making, Vol.5(3), pp.277-293
- Publisher
- SAGE Publications; Los Angeles, CA
- DOI
- 10.1177/1555343411415793
- ISSN
- 1555-3434
- eISSN
- 2169-5032
- Language
- English
- Date published
- 09/2011
- Academic Unit
- Industrial and Systems Engineering; Internal Medicine
- Record Identifier
- 9984064254702771
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