Joshua Tan

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About

Joshua Tan is a leading researcher in human-robot interaction and cognitive science, whose work explores the subtle dynamics of joint attention between humans and machines. His most-cited study, "Robot gaze does not reflexively cue human attention" (2011, 60 citations), challenges assumptions about social cueing by demonstrating that, unlike human gaze, robot gaze does not automatically trigger reflexive attention shifts. This foundational contribution reveals critical differences in how people process social versus robotic cues, informing the design of more intuitive and trustworthy autonomous systems. Tan’s research bridges experimental psychophysics and robotics, offering insights into the cognitive mechanisms underlying human-robot collaboration. His findings have implications for assistive technologies, autonomous vehicles, and social robots, where understanding attention dynamics is key to safe and effective interaction. With a growing body of work that continues to shape the field, Tan is recognized for his rigorous experimental approach and for raising important questions about the boundaries of social cognition in human-machine encounters.