An EEG-network-metric based approach to real-time trust inference in human-autonomy teaming.

Gregory Bales, Allison P A Hayman, Torin K Clark, Jason Dekarske, Sanjay Joshi, Zhaodan Kong

Abstract read

In one paragraph

Article in Frontiers in neuroergonomics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Gregory BalesCyber-Human-Physical Systems Lab, Department of Mechanical and Aerospace Engineering, University of California, Davis, Davis, CA, United States.

Allison P A HaymanAnn and H.J. Smead Aerospace Engineering Sciences, College of Engineering and Applied Science, University of Colorado, Boulder, CO, United States.

Torin K ClarkAnn and H.J. Smead Aerospace Engineering Sciences, College of Engineering and Applied Science, University of Colorado, Boulder, CO, United States.

Jason DekarskeCyber-Human-Physical Systems Lab, Department of Mechanical and Aerospace Engineering, University of California, Davis, Davis, CA, United States.

Sanjay JoshiCyber-Human-Physical Systems Lab, Department of Mechanical and Aerospace Engineering, University of California, Davis, Davis, CA, United States.

Zhaodan KongCyber-Human-Physical Systems Lab, Department of Mechanical and Aerospace Engineering, University of California, Davis, Davis, CA, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

autonomous robotscognitive processescognitive roboticselectroencephalographyhuman-autonomy teaminghuman-robot interactionintelligent automationnetwork theory

Identifiers

PMID41063843

DOI10.3389/fnrgo.2025.1627483

PMCPMC12500554

What OpenQuestion holds

Textmetadata

LicenceCC BY

Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.