On Wednesday, October 19th at noon in Tobin 521B, Dr. Brown will present:  Assessing the costs of visualizing uncertainty in electronic displays.

Feel free to pass along the invitation to others.  If you would like to hear her talk but can’t make it to Tobin, you are welcome to join on zoom
https://umass-amherst.zoom.us/j/97623669473?pwd=bU9aTVo1c1U3ZVBuVDJ3QmFwUmVHUT09
Meeting ID: 976 2366 9473
Passcode: Cog2223

Uncertainty in geospatial data refers to ambiguity that can arise from incomplete data, calculation errors, noisy equipment, and scaling issues to name a few (e.g., Pang, 2001; Pang, Wittenbrink, & Lodha, 1997).  In general, uncertainty is an inaccuracy that is not accounted for but is likely to result in user error because it occurs in finished products like maps and geospatial applications. However, visualizing uncertainty adds a layer of visual information, which is likely to increase the amount of visual clutter and can negatively impact usability (Beck, Trafton, & Lohrenz, 2010; Lohrenz & Beck, 2010). Additionally, processing of information presented in geospatial displays is heavily influenced by the layout, graphic design, and purpose which need to be taken into account (e.g., Hegarty, 2013; Liben, 2009; You, Chen, Liu, & Lin, 2007). Thus, there is a general need to display or visualize uncertainty to improve usability and decision making complicated by an increased load on cognitive processes by doing so.
The current study compared three techniques for visualizing geospatial uncertainty to determine if they differentially affected the cognitive processes required to search for targets within electronic nautical charts.  We manipulated the amount of clutter in the charts by varying the range of uncertainty and including fabricated wind, pressure and electronic navigation icons as additional geospatial layers.  As predicted, performance declined as the amount of clutter increased from all geospatial variables including uncertainty. Interestingly, the type of visualization had the greatest effect on performance. The visualization that was most distinct resulted in the worst performance, as it also increased the amount of visual clutter in the chart significantly more than the other uncertainty visualization types. The results have implications for the design and use of electronic maps of all types (road maps, maritime, and aeronautical charts).

Dr. Brown received her PhD in Psychology from Louisiana State University in 2011. She initially joined the US Naval Research Laboratory as a Karles Fellow with the Geospatial Human-Computer Interaction group at the Stennis Space Center where her research focused on decision support in geospatial displays. In 2018, she transferred to the Warfighter Applied Cognition and Technology Lab in DC where her research has pivoted to focus on individual differences in cognitive ability. Her research centers on basic and applied applications of attention, memory and decision making. Of particular interest are the ways in which individual differences in attention and memory can be leveraged to support Naval recruitment, selection and training.