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About

Megan M. Saylor is a developmental psychologist whose research illuminates how children and adults conceptualize agency, particularly the boundaries between living beings and machines. Her work centers on cognitive development and human-robot interaction, exploring how people reason about the minds and capabilities of humans, robots, and computers. In a landmark study (2010, 54 citations), Saylor investigated how young children categorize humanoid robots—entities that blur the line between living and non-living things—revealing that three- and four-year-olds flexibly attribute properties of both living things and machines to such novel artifacts. Her subsequent research (2011, 38 citations; 2012, 34 citations) demonstrated that adults intuitively distinguish between human and machine intelligence, expecting humans to engage in goal-directed, intentional behaviors while robots and computers rely on location- or feature-based processing. Saylor also developed a transition model for cognitions about agency (2013), providing a systematic framework for understanding how reasoning about different kinds of minds evolves. Her findings have profound implications for designing intuitive human-robot interactions and for educational approaches that help children grasp the nature of artificial intelligence. With over 175 cumulative citations, Saylor’s work bridges developmental psychology, cognitive science, and robotics, offering foundational insights into how we perceive and interact with increasingly intelligent machines.