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About

Elizabeth J. Goldman is a developmental psychologist whose research sits at the intersection of cognitive development, social cognition, and human-robot interaction. Her work focuses primarily on how young children perceive, categorize, and learn from social robots, exploring fundamental questions about anthropomorphism, animacy, and selective trust in early childhood. Goldman's most influential contribution, "People Do Not Always Know Best: Preschoolers' Trust in Social Robots" (2023, 32 citations), demonstrated that preschoolers are capable of preferring a competent robot over an incompetent human as a source of information — a striking finding that challenges assumptions about children's learning biases. Complementing this, her highly cited work on preschoolers' anthropomorphizing of robots (2023, 27 citations) examined how children's naïve biological reasoning shapes their judgments about robotic agents. Her 2024 review article on children's anthropomorphism of inanimate agents (18 citations) synthesizes a broad literature on animism and infant social cognition, establishing her as a thoughtful scholar of the field's foundations. More recently, her 2025 study applying the Theory of Mind Scale to robot protagonists pushes this agenda further into social-cognitive territory. Goldman's cumulative body of work, amassing nearly 100 citations within just a few years, positions her as an emerging leader in understanding how children navigate an increasingly robot-populated world.