Dylan Manfredi
Papers
2
Total Citations
95
H-Index
2
About
Dylan Manfredi is a leading researcher in human-robot interaction and perceptual psychology, best known for his groundbreaking work on the Uncanny Valley effect. His most influential study, “Uncanny but not confusing: Multisite study of perceptual category confusion in the Uncanny Valley” (2019), has garnered over 90 citations and fundamentally reshaped how researchers understand why highly realistic android robots can provoke feelings of eeriness and dislike. Manfredi’s major contribution was systematically testing the long-held hypothesis that category confusion—where observers cannot clearly classify a robot as either “human” or “machine”—drives this aversion. Through a rigorous multisite experimental design, his team demonstrated that perceptual uncertainty does not fully explain the Uncanny Valley phenomenon, challenging decades of theoretical assumptions and redirecting the field toward alternative mechanisms. This work has had significant impact across robotics, cognitive science, and human-computer interaction, influencing how engineers design socially acceptable robots. Manfredi’s research continues to inform the development of more comfortable and effective human-robot interfaces, making him a pivotal figure in understanding the psychological boundaries between humans and machines.
Research Focus
Key Achievements
Top Papers
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