Modeling the Dynamics of Nonverbal Behavior on Interpersonal Trust for Human-Robot Interactions
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 gestura...
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
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Association for the Advancement of Artificial Intelligence
2014
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| Online Access: | http://hdl.handle.net/1721.1/92378 https://orcid.org/0000-0002-0587-2065 https://orcid.org/0000-0003-1175-437X |
| _version_ | 1826211230258298880 |
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| author | Lee, Jin Joo Knox, Brad Breazeal, Cynthia Lynn |
| author2 | Massachusetts Institute of Technology. Media Laboratory |
| author_facet | Massachusetts Institute of Technology. Media Laboratory Lee, Jin Joo Knox, Brad Breazeal, Cynthia Lynn |
| author_sort | Lee, Jin Joo |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T15:02:38Z |
| format | Article |
| id | mit-1721.1/92378 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T15:02:38Z |
| publishDate | 2014 |
| publisher | Association for the Advancement of Artificial Intelligence |
| record_format | dspace |
| spelling | mit-1721.1/923782022-09-29T12:14:38Z Modeling the Dynamics of Nonverbal Behavior on Interpersonal Trust for Human-Robot Interactions Lee, Jin Joo Knox, Brad Breazeal, Cynthia Lynn Massachusetts Institute of Technology. Media Laboratory Program in Media Arts and Sciences (Massachusetts Institute of Technology) Lee, Jin Joo Knox, Brad Breazeal, Cynthia Lynn 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. 2014-12-18T17:35:33Z 2014-12-18T17:35:33Z 2013-03 Article http://purl.org/eprint/type/JournalArticle http://hdl.handle.net/1721.1/92378 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. https://orcid.org/0000-0002-0587-2065 https://orcid.org/0000-0003-1175-437X en_US https://www.aaai.org/ocs/index.php/SSS/SSS13/paper/view/5804/6013 Proceedings of the 2013 AAAI Spring Symposium Series Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Association for the Advancement of Artificial Intelligence MIT web domain |
| spellingShingle | Lee, Jin Joo Knox, Brad Breazeal, Cynthia Lynn Modeling the Dynamics of Nonverbal Behavior on Interpersonal Trust for Human-Robot Interactions |
| title | Modeling the Dynamics of Nonverbal Behavior on Interpersonal Trust for Human-Robot Interactions |
| title_full | Modeling the Dynamics of Nonverbal Behavior on Interpersonal Trust for Human-Robot Interactions |
| title_fullStr | Modeling the Dynamics of Nonverbal Behavior on Interpersonal Trust for Human-Robot Interactions |
| title_full_unstemmed | Modeling the Dynamics of Nonverbal Behavior on Interpersonal Trust for Human-Robot Interactions |
| title_short | Modeling the Dynamics of Nonverbal Behavior on Interpersonal Trust for Human-Robot Interactions |
| title_sort | modeling the dynamics of nonverbal behavior on interpersonal trust for human robot interactions |
| url | http://hdl.handle.net/1721.1/92378 https://orcid.org/0000-0002-0587-2065 https://orcid.org/0000-0003-1175-437X |
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