Computationally Modeling Interpersonal Trust
We present a computational model capable of predicting—above human accuracy—the degree of trust a person has toward their novel partner by observing the trust-related nonverbal cues expressed in their social interaction. We summarize our prior work, in which we identify nonverbal cues that signal un...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2013-12-01
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| Series: | Frontiers in Psychology |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fpsyg.2013.00893/full |
| _version_ | 1811218326046638080 |
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| author | Jin Joo eLee Brad eKnox Jolie eBaumann Cynthia eBreazeal David eDeSteno |
| author_facet | Jin Joo eLee Brad eKnox Jolie eBaumann Cynthia eBreazeal David eDeSteno |
| author_sort | Jin Joo eLee |
| collection | DOAJ |
| description | We present a computational model capable of predicting—above human accuracy—the degree of trust a person has toward their novel partner by observing the trust-related nonverbal cues expressed in their social interaction. We summarize our prior work, in which we identify nonverbal cues that signal untrustworthy behavior and also demonstrate the human mind’s readiness to interpret those cues to assess the trustworthiness of a social robot. We demonstrate that domain knowledge gained from our prior work using human-subjects experiments, when incorporated into the feature engineering process, permits a computational model to outperform both human predictions and a baseline model built in naivete' of this domain knowledge. We then present the construction of hidden Markov models to incorporate temporal relationships among the trust-related nonverbal cues. By interpreting the resulting learned structure, we observe that models built to emulate different levels of trust exhibit different sequences of nonverbal cues. From this observation, we derived sequence-based temporal features that further improve the accuracy of our computational model. Our multi-step research process presented in this paper combines the strength of experimental manipulation and machine learning to not only design a computational trust model but also to further our understanding of the dynamics of interpersonal trust. |
| first_indexed | 2024-04-12T07:08:42Z |
| format | Article |
| id | doaj.art-db94a82e665d4ec09ebbc219b3b88b38 |
| institution | Directory Open Access Journal |
| issn | 1664-1078 |
| language | English |
| last_indexed | 2024-04-12T07:08:42Z |
| publishDate | 2013-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Psychology |
| spelling | doaj.art-db94a82e665d4ec09ebbc219b3b88b382022-12-22T03:42:44ZengFrontiers Media S.A.Frontiers in Psychology1664-10782013-12-01410.3389/fpsyg.2013.0089356004Computationally Modeling Interpersonal TrustJin Joo eLee0Brad eKnox1Jolie eBaumann2Cynthia eBreazeal3David eDeSteno4Massachusetts Institute of TechnologyMassachusetts Institute of TechnologyNortheastern UniversityMassachusetts Institute of TechnologyNortheastern UniversityWe present a computational model capable of predicting—above human accuracy—the degree of trust a person has toward their novel partner by observing the trust-related nonverbal cues expressed in their social interaction. We summarize our prior work, in which we identify nonverbal cues that signal untrustworthy behavior and also demonstrate the human mind’s readiness to interpret those cues to assess the trustworthiness of a social robot. We demonstrate that domain knowledge gained from our prior work using human-subjects experiments, when incorporated into the feature engineering process, permits a computational model to outperform both human predictions and a baseline model built in naivete' of this domain knowledge. We then present the construction of hidden Markov models to incorporate temporal relationships among the trust-related nonverbal cues. By interpreting the resulting learned structure, we observe that models built to emulate different levels of trust exhibit different sequences of nonverbal cues. From this observation, we derived sequence-based temporal features that further improve the accuracy of our computational model. Our multi-step research process presented in this paper combines the strength of experimental manipulation and machine learning to not only design a computational trust model but also to further our understanding of the dynamics of interpersonal trust.http://journal.frontiersin.org/Journal/10.3389/fpsyg.2013.00893/fullmachine learninghuman-robot interactionnonverbal behavior analysissocial signal processingcomputational trust modelinterpersonal trust |
| spellingShingle | Jin Joo eLee Brad eKnox Jolie eBaumann Cynthia eBreazeal David eDeSteno Computationally Modeling Interpersonal Trust Frontiers in Psychology machine learning human-robot interaction nonverbal behavior analysis social signal processing computational trust model interpersonal trust |
| title | Computationally Modeling Interpersonal Trust |
| title_full | Computationally Modeling Interpersonal Trust |
| title_fullStr | Computationally Modeling Interpersonal Trust |
| title_full_unstemmed | Computationally Modeling Interpersonal Trust |
| title_short | Computationally Modeling Interpersonal Trust |
| title_sort | computationally modeling interpersonal trust |
| topic | machine learning human-robot interaction nonverbal behavior analysis social signal processing computational trust model interpersonal trust |
| url | http://journal.frontiersin.org/Journal/10.3389/fpsyg.2013.00893/full |
| work_keys_str_mv | AT jinjooelee computationallymodelinginterpersonaltrust AT bradeknox computationallymodelinginterpersonaltrust AT jolieebaumann computationallymodelinginterpersonaltrust AT cynthiaebreazeal computationallymodelinginterpersonaltrust AT davidedesteno computationallymodelinginterpersonaltrust |