Browsing by Author "Noble, Kayla M."

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How history trails and set size influence detection of hostile intentions
(Springer Science and Business Media LLC, 2022-05) Patton, Colleen E.; Wickens, Christopher D.; Clegg, Benjamin A.; Noble, Kayla M.; Smith, C. A. P.
Previous research suggests people struggle to detect a series of movements that might imply hostile intentions of a vessel, yet this ability is crucial in many real world Naval scenarios. To investigate possible mechanisms for improving performance, participants engaged in a simple, simulated ship movement task. One of two hostile behaviors were present in one of the vessels: Shadowing—mirroring the participant’s vessel’s movements; and Hunting—closing in on the participant’s vessel. In the first experiment, history trails, showing the previous nine positions of each ship connected by a line, were introduced as a potential diagnostic aid. In a second experiment, the number of computer-controlled ships on the screen also varied. Smaller set size improved detection performance. History trails also consistently improved detection performance for both behaviors, although still falling well short of optimal, even with the smaller set size. These findings suggest that working memory plays a critical role in performance on this dynamic decision making task, and the constraints of working memory capacity can be decreased through a simple visual aid and an overall reduction in the number of objects being tracked. The implications for the detection of hostile intentions are discussed.
Supporting detection of hostile intentions: automated assistance in a dynamic decision-making context
(Springer Science and Business Media LLC, 2023-11) Patton, Colleen E.; Wickens, Christopher D.; Smith, C. A. P.; Noble, Kayla M.; Clegg, Benjamin A.
In a dynamic decision-making task simulating basic ship movements, participants attempted, through a series of actions, to elicit and identify which one of six other ships was exhibiting either of two hostile behaviors. A high-performing, although imperfect, automated attention aid was introduced. It visually highlighted the ship categorized by an algorithm as the most likely to be hostile. Half of participants also received automation transparency in the form of a statement about why the hostile ship was highlighted. Results indicated that while the aid’s advice was often complied with and hence led to higher accuracy with a shorter response time, detection was still suboptimal. Additionally, transparency had limited impacts on all aspects of performance. Implications for detection of hostile intentions and the challenges of supporting dynamic decision making are discussed.