Ya Fan

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About

Ya Fan is a researcher in human-robot interaction and social cognition, whose work explores how people perceive and engage with humanoid robots. Her most-cited paper, "People Do not Automatically Take the Level-1 Visual Perspective of Humanoid Robot Avatars" (2021, 18 citations), challenges assumptions about automatic perspective-taking in human-robot encounters. By demonstrating that humans do not instinctively adopt a robot's visual viewpoint—unlike with other humans—Fan's research reveals critical asymmetries in social cognition that inform the design of more intuitive robotic interfaces. This finding has implications for fields ranging from virtual reality to assistive robotics, where understanding user perspective is key to effective collaboration. Fan’s contributions bridge cognitive psychology and robotics, offering empirical evidence that shapes how researchers think about anthropomorphism and social presence. Her work is particularly notable for its methodological rigor and its challenge to prevailing theories of automatic mentalizing, making it a touchstone for studies on human-robot trust and communication. With growing interest in embodied AI, Fan’s insights continue to guide the development of robots that better align with human social expectations.