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Joel M. Cooper; Kaedyn W. Crabtree; Amy S. McDonnell; Dominik May; Sean C. Strayer; Tushig Tsogtbaatar; Danielle R. Cook; Parker A. Alexander; David M. Sanbonmatsu; David L. Strayer – Cognitive Research: Principles and Implications, 2023
Vehicle automation is becoming more prevalent. Understanding how drivers use this technology and its safety implications is crucial. In a 6-8 week naturalistic study, we leveraged a hybrid naturalistic driving research design to evaluate driver behavior with Level 2 vehicle automation, incorporating unique naturalistic and experimental control…
Descriptors: Motor Vehicles, Automation, Information Technology, Behavior
Daniel J. Carragher; Daniel Sturman; Peter J. B. Hancock – Cognitive Research: Principles and Implications, 2024
The human face is commonly used for identity verification. While this task was once exclusively performed by humans, technological advancements have seen automated facial recognition systems (AFRS) integrated into many identification scenarios. Although many state-of-the-art AFRS are exceptionally accurate, they often require human oversight or…
Descriptors: Automation, Human Body, Man Machine Systems, Accuracy
Transparency Improves the Accuracy of Automation Use, but Automation Confidence Information Does Not
Monica Tatasciore; Luke Strickland; Shayne Loft – Cognitive Research: Principles and Implications, 2024
Increased automation transparency can improve the accuracy of automation use but can lead to increased bias towards agreeing with advice. Information about the automation's confidence in its advice may also increase the predictability of automation errors. We examined the effects of providing automation transparency, automation confidence…
Descriptors: Automation, Access to Information, Information Technology, Bias