Visualizing Uncertainty: The Impact on Performance. (May 2014)

• Record Type: Journal Article
• Authors: Kirschenbaum, Susan S.; Trafton, J. Gregory; Schunn, Christian D.; Trickett, Susan B.
• Is Part Of: Human factors. Volume 56:Number 3(2014)
• Page Start: 509
• Page End: 520
• Publication Date: 2014-05
• Subjects: decision making -- naturalistic decision making -- cognitive processes -- knowledge representation -- problem solving -- reasoning; Human engineering -- Periodicals; 620.82
• DOI: 10.1177/0018720813498093
• Languages: English
• ISSNs: 0018-7208

Abstract

Objective: This work investigated the impact of uncertainty representation on performance in a complex authentic visualization task, submarine localization. Background: Because passive sonar does not provide unique course, speed, and range information on a contact, the submarine operates under significant uncertainty. There are many algorithms designed to address this problem, but all are subject to uncertainty. The extent of this solution uncertainty can be expressed in several ways, including a table of locations (course, speed, range) or a graphical area of uncertainty. Method: To test the hypothesis that the representation of uncertainty that more closely matches the experts' preferred representation of the problem would better support performance, even for the nonexpert., performance data were collected using displays that were either stripped of the spatial or the tabular representation. Results: Performance was more accurate when uncertainty was displayed spatially. This effect was only significant for the nonexperts for whom the spatial displays supported almost expert-like performance. This effect appears to be due to reduced mental effort. Conclusion: These results suggest that when the representation of uncertainty for this spatial task better matches the expert's preferred representation of the problem even a nonexpert can show expert-like performance. Application: These results could apply to any domain where performance requires working with highly uncertain information.