Modeling the Dynamics of Nonverbal Behavior on Interpersonal Trust for Human-Robot Interactions

• Main Authors: Lee, Jin Joo (Contributor), Knox, Brad (Contributor), Breazeal, Cynthia Lynn (Contributor)
• Other Authors: Massachusetts Institute of Technology. Media Laboratory (Contributor), Program in Media Arts and Sciences (Massachusetts Institute of Technology) (Contributor)
• Format: Article
• Language: English
• Published: Association for the Advancement of Artificial Intelligence, 2014-12-18T17:35:33Z.
• Subjects: Article
• Online Access: Get fulltext

 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.