Computational Modeling of the Dynamics of Human Trust during Human-Machine Interactions

Wan Lin Hu, Kumar Akash, Tahira Reid, Neera Jain

Research output: Contribution to journalArticlepeer-review

48 Scopus citations

Abstract

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.

Original languageEnglish (US)
Article number8502860
Pages (from-to)485-497
Number of pages13
JournalIEEE Transactions on Human-Machine Systems
Volume49
Issue number6
DOIs
StatePublished - Dec 2019

All Science Journal Classification (ASJC) codes

  • Human Factors and Ergonomics
  • Control and Systems Engineering
  • Signal Processing
  • Human-Computer Interaction
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence

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