Human decision makers typically use heuristics under time-pressured situations. These heuristics can potentially degrade task performance through the impact of their associated biases. Using object identification in image analysis as the context, this paper identifies cognitive biases that play a role in decision making. We propose a decision support system to help overcome these biases in this context. Results show that the decision support system improved human decision making in object identification, including metrics such as time taken to identify targets in an image set, accuracy of target identification, accuracy of target classification, and quantity of false positive identification. 1. Introduction As the growth of sensor technology outpaces the analyst’s ability to process captured images, object identification within the military image analysis task has become an increasingly time-critical human problem-solving task [1]. Intuitively, in this information-rich domain, the pressure associated with time-critical decision making can lead human operators to deploy a variety of techniques to alleviate the time pressure. When this time pressure persists, the decision maker often changes their cognitive processing methods, leading to the use of cognitive heuristics and their resulting biases. Cognitive heuristics are rules-of-thumb employed during decision making that can lead to biases that degrade the quality of decisions. Huey and Wickens [2] identify how heuristics and biases impact decision making through the distortion of hypothesis formulation and situation awareness. They also conclude that this distortion, which can degrade decision making, can occur during information processing. Pioneering work by Tversky and Kahneman [3] and others in the judgmental decision making field [4–8] identifies several heuristics and biases that commonly appear during decision-making tasks. Although much research has been done on the effects of biases in judgmental decision making tasks, there has been little work done that specifically identifies cognitive biases within a time-critical task such as object identification. Thus there is a need to understand potential biases and develop support systems to mitigate their negative impacts, thereby aiding the analyst [9]. While decision support tools such as algorithms are currently being developed, they are presently not employed extensively by image analysts (IAs) in field settings [10]. Clearly, the dearth of tools indicates further work is needed to develop effective decision support methods to relieve the
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