Tyler Egan
Papers
1
Total Citations
8
H-Index
1
About
Tyler Egan is a researcher whose work sits at the intersection of human-robot interaction, social perception, and language processing. His research explores how people anthropomorphize robots in spoken dialogue settings, with a particular focus on the perceived social characteristics—such as age and educational level—that humans assign to robotic systems. In his most-cited paper, "Predicting Perceived Age: Both Language Ability and Appearance are Important" (2018, 8 citations), Egan demonstrates that both a robot’s linguistic competence and its physical appearance significantly shape how human dialogue partners infer its age and social standing. This work is notable for its situated, ecologically valid approach, using real-time spoken interactions to uncover the subtle cues that drive anthropomorphism. While his citation count is modest, Egan’s contributions are foundational for designing more socially aware robots, particularly in contexts where perceived age or educational level influences trust and communication. His research offers valuable insights for students and researchers interested in the social dynamics of human-robot interaction, language-based perception, and the design of conversational agents that are not only functional but socially intuitive.
Research Focus
Key Achievements
Top Papers
- 1