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About

Matthew S. Prewett is a researcher specializing in human-robot interaction (HRI), with a particular focus on operator workload, automation reliability, and the cognitive demands placed on humans managing robotic systems. His work sits at the intersection of applied psychology, human factors, and robotics, addressing critical questions about how humans effectively supervise and collaborate with increasingly autonomous machines. Prewett's most influential contribution, "Managing Workload in Human–Robot Interaction: A Review of Empirical Studies" (2010), has accumulated 122 citations, establishing him as a meaningful voice in understanding how cognitive workload shapes HRI outcomes. This review synthesized empirical evidence to help researchers and designers better manage the mental demands on robot operators. His companion work applying multiple resources theory to teleoperation workload revealed that controlling more than two robotic platforms simultaneously degrades key performance metrics such as reaction time and error rate — a practically significant finding for military, emergency response, and industrial robotics design. His qualitative reviews on autonomy and automation reliability further underscore a consistent research theme: as robots assume more complex tasks, the human-automation relationship must be carefully calibrated to remain effective. For students entering HRI or human factors research, Prewett's work offers foundational frameworks for understanding the cognitive boundaries of human supervisory control.