Journal
IEEE TRANSACTIONS ON HUMAN-MACHINE SYSTEMS
Volume 49, Issue 6, Pages 485-497Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/THMS.2018.2874188
Keywords
Stability analysis; Autonomous systems; Data models; Human computer interaction; Cultural differences; Behavioral sciences; Affective computing; Affective computing; autonomous systems; behavioral sciences; cultural differences; data models; human-computer interaction; man-machine systems; stability analysis
Funding
- National Science Foundation [1548616]
- Directorate For Engineering
- Div Of Chem, Bioeng, Env, & Transp Sys [1548616] Funding Source: National Science Foundation
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We developed an experiment to elicit human trust dynamics in human-machine interaction contexts and established a quantitative model of human trust behavior with respect to these contexts. The proposed model describes human trust level as a function of experience, cumulative trust, and expectation bias. We estimated the model parameters using human subject data collected from two experiments. Experiment 1 was designed to excite human trust dynamics using multiple transitions in trust level. Five hundred and eighty-one individuals participated in this experiment. Experiment 2 was an augmentation of Experiment 1 designed to study and incorporate the effects of misses and false alarms in the general model. Three hundred and thirty-three individuals participated in Experiment 2. Beyond considering the dynamics of human trust in automation, this model also characterizes the effects of demographic factors on human trust. In particular, our results show that the effects of national culture and gender on trust are significant. For example, U.S. participants showed a lower trust level and were more sensitive to misses as compared with Indian participants. The resulting trust model is intended for the development of autonomous systems that can respond to changes in human trust level in real time.
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