ORIGINAL RESEARCH article
Front. Neuroergonomics
Sec. Neurotechnology and Systems Neuroergonomics
Volume 6 - 2025 | doi: 10.3389/fnrgo.2025.1627483
An EEG-Network-Metric Based Approach to Real-Time Trust Inference in Human-Autonomy Teaming
Provisionally accepted
- 1University of California, Davis, Davis, United States
- 2University of Colorado, Boulder, Boulder, United States
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Efficient and effective teaming between humans and autonomous systems requires the establishment and maintenance of trust to maximize team task performance. Despite advances in autonomous systems, human expertise remains critical in tasks fraught with deviations from procedures or plans that cannot be pre-programmed. As autonomous systems become more sophisticated, they will possess the ability to positively influence interactions with their human partners, provided the autonomous systems have a real-time estimation of their human partner's cognitive state (including trust). In this paper, we report our results in ascertaining a human's trust in an autonomous system via electroencephalogram (EEG) measurements. We report that trust can be measured continuously and unobtrusively, and that using analysis techniques which account for interactions among brain regions shows benefits compared to more traditional methods which use only EEG signal-power. Inter-channel connectivity network-metrics, which measure dynamic changes in synchronous behavior between distant brain regions, appear to better capture cognitive activities that correlate with a human's trust in an autonomous system.
Keywords: human-robot interaction, Human-Autonomy teaming, Autonomous Robots, Intelligent automation, Cognitive Robotics, Electroencephalography, Cognitive Processes, Network theory Frontiers
Received: 12 May 2025; Accepted: 27 Aug 2025.
Copyright: © 2025 Bales, Hayman, Clark, Dekarske, Joshi and Kong. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Zhaodan Kong, University of California, Davis, Davis, United States
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