Examining Human Decision Making in Automated Combat Identification Systems using Signal Detection Theory

The purpose of this research was to examine the effects of combat identification systems on a soldier’s engagement decision. The research investigated changes in target identification performance and response biases across different simulated battlefield and aid conditions. These conditions were manipulated by changing the costs of making an error and the availability of an aid. Signal detection theory served as the basis for this study and its analytical model. 19 university students were recruited to interact with a combat identification simulation and were asked to identify and engage targets in six experiment blocks presented in a random order. Target identification performance results indicated a significant effect of aid condition, but not in cost of errors. Participants always made fewer errors in the presence of an aid, regardless of the costs of error. In addition, response bias results revealed no significant effects of aid condition, cost of error, or the interaction of both factors. Generally, results indicated that the response bias for all conditions was not significantly different from the optimal value, illustrating that participants adjusted their response bias appropriately. This research has implications for the design of operation briefings and engagement training for soldiers. Findings from this research can be used to help military personnel better understand the effects of combat identification systems on their decision making, thereby improving the level of reliance in these systems, which can contribute to a reduction in the risk of friendly fire incidents. Acknowledgements We would like to express our deepest gratitude to everyone who made it possible for us to complete this thesis. We would, in particular, like to thank our supervisor Professor Greg A. Jamieson for offering us the opportunity to work on this project and for all the advice and knowledge that he so openly shared with us. We would also like to gratefully thank Lu Wang for her continued support and guidance, especially during the first few months while working on the thesis. It was a privilege to have learned from her. We owe many thanks to her. Lastly, we would like to thank our family and friends for their continued love and support.