Sarah Plane

Papers

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About

Sarah Plane’s research lies at the compelling intersection of human-robot interaction, sociolinguistics, and cognitive science. Her work fundamentally explores how humans anthropomorphize robots during spoken dialogue, revealing that we assign social characteristics—like age and education level—based on a complex interplay of language ability and physical appearance. In her most cited work, "Predicting Perceived Age: Both Language Ability and Appearance are Important" (2018, 8 citations), Plane demonstrates that a robot’s perceived age is not simply a function of its design, but is profoundly shaped by its linguistic competence. This insight challenges simplistic views of robot design, showing that communication style is as critical as hardware. In related work (2017), she pioneers the application of the "words-as-classifiers" model of lexical semantics to grounding tasks with toy robots like Anki's Cozmo, advancing how machines achieve shared understanding with humans across symbolic, conversational, and societal levels. By bridging high-level linguistic theory with tangible robotic platforms, Plane’s research provides a vital framework for designing more intuitive, socially-aware agents—a contribution that is essential reading for anyone interested in the future of natural, human-like machine interaction.