The Uncanny Valley

March 1, 2012
2:50 pm - 4:00 pm
Halligan 111


Abstract: It seems natural to assume that the more closely robots, avatars, and animated characters come to resemble people, the more likely they are to elicit the kinds of responses people direct toward each other. However, subtle nonhuman aspects of form, motion quality, and mutually-contingent interaction only seem eerie in very humanlike entities. This uncanny phenomenon may be symptomatic of entities that elicit, but fail to satisfy, a model of a human other. If so, a very humanlike robot or interactive character may provide the best means of discovering what kinds of behavior are perceived as human, because deviations from human norms are more salient. In pursuing this line of inquiry, it is essential to identify the cognitive mechanisms involved in evaluations of humanness. This presentation will review the current research on the uncanny valley.

Biography: Karl F. MacDorman is an associate professor in the School of Informatics, Indiana University. Dr. MacDorman received his Bachelor of Arts degree in computer science from University of California, Berkeley in 1988 and his Ph.D. in machine learning and robotics from Cambridge University in 1996.

Most recently MacDorman was an associate professor at Osaka University, Japan (2003-2005). Previously, he was assistant professor in the Department of Systems and Human Science at the same institution (1997-2000), and a supervisor (1991-1997) and research fellow (1997- 1998) at Cambridge University. Dr. MacDorman has also worked as a software engineer at Sun Microsystems and as chief technology officer for two venture companies. His research focuses on human-robot interaction and the symbol grounding problem. He has co-organized the workshop Toward Social Mechanisms of Android Science at CogSci 2005 and CogSci/ICCS 2006, the workshop Views of the Uncanny Valley at IEEE Humanoids 2005, and the special session Psychological Benchmarks of Human-Robot Interaction at IEEE Ro-Man 2006 and has edited special issues on these topics for Connection Science and Interaction Studies. He has published extensively in robotics, machine learning, and cognitive science.