Yao Song
Papers
9
Total Citations
323
H-Index
7
About
Yao Song is a prominent researcher specializing in human-robot interaction, social robot design, and artificial intelligence, with a particular focus on the critical intersection of facial anthropomorphism and trustworthiness. Their work has fundamentally advanced our understanding of how robot facial features influence human perception and acceptance of AI agents. Song's most influential contribution, a 2020 systematic review on facial anthropomorphic trustworthiness in social robots (96 citations), established a foundational framework for the field, synthesizing scattered research into actionable design principles. Building on this, their subsequent studies examined specific facial features, face ratios, and emotional expressions as drivers of consumer trust and preference, collectively accumulating nearly 300 citations across their top works. Their development of a dedicated Facial Anthropomorphic Trustworthiness Scale represents a methodological milestone, offering researchers and designers a validated measurement tool for evaluating robot trustworthiness during initial human encounters. Beyond technical design, Song's research extends into applied domains, exploring how baby robots might address demographic challenges and how arts and design intersect with public health. Their multidisciplinary approach — bridging psychology, design, and AI — positions them as a uniquely versatile voice shaping the future of socially intelligent robotics.
Research Focus
Key Achievements
Top Papers
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- 3The face of trust: The effect of robot face ratio on consumer preference54 citations · 2020
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- 8When Trustworthiness Meets Face: Facial Design for Social Robots4 citations · 2024
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