Due to an abundance of data and dynamic nature of tasks, challenges with information retrieval in surveillance and target identification tasks have risen in today's Intelligence, Surveillance, and Reconnaissance (ISR) community. In this study, two variables, Area of Coverage and Amount of Activity (AOC/ACT), are manipulated to study their effects on the number of Watch Windows an observer can monitor. This research describes the analyst's task model, and explains how the level of AOC/ACT and number of Watch Windows affects the analyst's cognitive load. Results showed a significant difference in performance and physiological indicators of workload between high AOC/ACT conditions and low AOC/ACT conditions. A linear correlation was exhibited between the number of Watch Windows and the number of fixations. The results show that these variables can be manipulated in tasking to maintain appropriate levels of cognitive workload.
TopBackground
Intelligence Surveillance and Reconnaissance (ISR) is a critical community to the U.S Department of Defense (DoD) that account for about $40 billion annually; and its functions involve various methods of information acquisition for national security decision makers (Best Jr., 2005). An Intelligence Analyst or Image Analyst (IA) is the person who specializes in performing tasks such as monitoring, detection and recognition of various targets. Their tasks can be performed using either still imagery or motion imagery. IAs can perform static or dynamic recognition activities. Static refers to detection and recognition of objects while dynamic refers to detection and recognition of activities (Irvine et al., 2005).
Because information processing currently involves observers watching multiple windows, the ISR community faces several challenges in the area of visual surveillance. These challenges involve information overload, information fusion, and fleeting targets that appear within a short period of time, thus demanding a quick response, and relocatable targets (Barber, 2001; Duncan & Ayache, 2000; Jones, Shapiro & Roshon, 2007; Pham, Cirincione, Verma, & Pearson, 2008). The term watch window refers to a geographic area on a computer screen on which an observer is tasked to observe and report. Accuracy of the observer’s decision-making in a watch window study involves signal detection, which is based on odds that favor certain possibilities of outcomes (McNicol, 2005; Abbot & Sherrat, 2013; Wixted, 2007; Hautus, O'Mahony, & Lee, 2008; Pleskac & Busemeyer, 2010; O’Mahony & Hautus, 2008; Verghese, 1994, 2001; Palmer, Ames, & Lindsey, 1993; Eckstein, 2000; Ramos-Alvarez, 2012).