Inferring Trust From Users’ Behaviours; Agents’ Predictability Positively Affects Trust, Task Performance and Cognitive Load in Human-Agent Real-Time Collaboration
Open accessAbstract
Collaborative virtual agents help human operators to perform tasks in real-time. For this collaboration to be effective, human operators must appropriately trust the agent(s) they are interacting with. Multiple factors influence trust, such as the context of interaction, prior experiences with automated systems and the quality of the help offered by agents in terms of its transparency and performance. Most of the literature on trust in automation identified the performance of the agent as a key factor influencing trust. However, other work has shown that the behavior of the agent, type of the agent’s errors, and predictability of the agent’s actions can influence the likelihood of the user’s reliance on the agent and efficiency of tasks completion. Our work focuses on how agents’ predictability affects cognitive load, performance and users’ trust in a real-time human-agent collaborative task. We used an interactive aiming task where participants had to collaborate with different agents that varied in terms of their predictability and performance. This setup uses behavioral information (such as task performance and reliance on the agent) as well as standardized survey instruments to estimate participants’ reported trust in the agent, cognitive load and perception of task difficulty. Thirty participants took part in our lab-based study. Our results showed that agents with more predictable behaviors have a more positive impact on task performance, reliance and trust while reducing cognitive workload. In addition, we investigated the human-agent trust relationship by creating models that could predict participants’ trust ratings using interaction data. We found that we could reliably estimate participants’ reported trust in the agents using information related to performance, task difficulty and reliance. This study provides insights on behavioral factors that are the most meaningful to anticipate complacent or distrusting attitudes toward automation. With this work, we seek to pave the way for the development of trust-aware agents capable of responding more appropriately to users by being able to monitor components of the human-agent relationships that are the most salient for trust calibration.
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The need for trust in collaborative tasks might increase for situations of human-AI teaming, where the human team member is dependent on the performance of the system.
“However, need for trust in collaborative tasks might increase for situations of human-AI teaming, where the human team member is dependent on the performance of the system, such as [18,24,116] report.”
From Measuring and Understanding Trust Calibrations for Automated Systems: A Survey of the State-Of-The-Art and Future Directions (2023) · cites this paper as [24]
The Abstract says effective human-agent collaboration requires appropriate trust and identifies agent performance as a factor influencing it. The Introduction likewise calls trust important in collaborative decision-making. Neither passage says the need for trust increases when a human teammate depends on the system’s performance. (LLM, conf=0.82)
Experimental — accuracy not yet measuredSome authors use correlations between perceived trustworthiness and system capabilities to measure calibration; larger correlation indicates better calibration.
“Some authors work with correlations to get a better sense of perceived trustworthiness and system capabilities [5,12,24,39,66,67,81,94,130,139,142].Here the larger the correlation between perceived trustworthiness and system capabilities, the better the calibration.”
From Measuring and Understanding Trust Calibrations for Automated Systems: A Survey of the State-Of-The-Art and Future Directions (2023) · cites this paper as [24]
Section 4.5, “Predicting Trust” (page unavailable), reports correlations between participants’ trust ratings and performance measures, supporting the first part of the claim. Section 2.1 discusses trust calibration, but the paper does not say that a larger trust–capability correlation means better calibration; Section 4.5 describes a higher correlation only as a stronger association. (LLM, conf=0.86)
Experimental — accuracy not yet measured
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DSPT: Disassembly Sequence Planning Transformer for Interaction Guidance in VR
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Trust in the Loop: Building and Maintaining Human Trust in AI Collaborative Systems
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- Authors
- Daronnat, S., Azzopardi, L., Halvey, M., & Dubiel, M.
- Affiliations
- University of Strathclyde (GB)
- Corresponding author
- Daronnat, Sylvain
- Venue
- Frontiers in Robotics and AI
- Publisher
- Frontiers Media SA
- Published
- 2021-07-08
- Volume
- 8
- Pages
- 642201-642201
- ISSN
- 2296-9144 (ISSN-L)
- PMC
- PMC8295498
- OpenAlex
- W3181832363
- Cited by
- 45
- References
- 54
- Citations by year
- 2026: 11 · 2025: 12 · 2024: 9 · 2023: 9 · 2022: 4
- FWCI
- 5.39
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Robotics and AI (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Strathprints: The University of Strathclyde institutional repository (University of Strathclyde) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- University of Strathclyde
- Topics
- Social Sciences › Psychology › Social Psychology › Human-Automation Interaction and Safety
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Physical Sciences › Computer Science › Human-Computer Interaction › Usability and User Interface Design
- Keywords
- trust in automation, human-agent collaboration, cognitive load, task performance, reliance on automation, trust calibration, real-time collaboration
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