Modeling the Dynamics of Nonverbal Behavior on Interpersonal Trust for Human-Robot Interactions
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Author(s)
Lee, Jin Joo
Knox, Brad
Breazeal, Cynthia Lynn
Date Issued
March 2013
Journal
Proceedings of the 2013 AAAI Spring Symposium Series
Publisher
Association for the Advancement of Artificial Intelligence
Citation
Lee, Jin Joo, Brad Knox, and Cynthia Breazeal. "Modeling the Dynamics of Nonverbal Behavior on Interpersonal Trust for Human-Robot Interactions." The 2013 AAAI Spring Symposium Series, Stanford, California, March 2013.
Version
Author's final manuscript
Abstract
We describe research towards creating a computational model for recognizing interpersonal trust in social interactions. We found that four negative gestural cues—leaning-backward, face-touching, hand-touching, and crossing-arms—are together predictive of lower levels of trust. Three positive gestural cues—leaning-forward, having arms-in-lap, and open-arms—are predictive of higher levels of trust. We train a probabilistic graphical model using natural social interaction data, a “Trust Hidden Markov Model” that incorporates the occurrence of these seven important gestures throughout the social interaction. This Trust HMM predicts with 69.44% accuracy whether an individual is willing to behave cooperatively or uncooperatively with their novel partner; in comparison, a gesture-ignorant model achieves 63.89% accuracy. We attempt to automate this recognition process by detecting those trust-related behaviors through 3D motion capture technology and gesture recognition algorithms. We aim to eventually create a hierarchical system—with low-level gesture recognition for high-level trust recognition—that is capable of predicting whether an individual finds another to be a trustworthy or untrustworthy partner through their nonverbal expressions.
MIT Department
Massachusetts Institute of Technology. Media Laboratory
Program in Media Arts and Sciences (Massachusetts Institute of Technology)
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://www.aaai.org/ocs/index.php/SSS/SSS13/paper/view/5804/6013
