Trust Calibration through Reliability Displays in Automated Vehicles

Noah, Brittany E.; Walker, Bruce N.

doi:10.1145/3029798.3034802

Cited by 18 publications

(7 citation statements)

References 7 publications

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“…Trust, as a result of multiple cognitive pro- Huang & Bashir, 2017;Kun et al, 2018;Lee & See, 2000;Noah & Walker, 2017;Pop et al, 2015;Thropp et al, 2018;Visser et al, 2014;Yeh & Wickens, 2001). Among them, reliability and credibility are two of the most researched factors in human-automation interaction (Balfe et al, 2018;Lee & See, 2004;Madhavan & Wiegmann, 2007).…”

Section: Influencing Factors Of Trustmentioning

confidence: 99%

Evaluating Effective Connectivity of Trust in Human–Automation Interaction: A Dynamic Causal Modeling (DCM) Study

et al. 2021

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Objective Using dynamic causal modeling (DCM), we examined how credibility and reliability affected the way brain regions exert causal influence over each other—effective connectivity (EC)—in the context of trust in automation. Background Multiple brain regions of the central executive network (CEN) and default mode network (DMN) have been implicated in trust judgment. However, the neural correlates of trust judgment are still relatively unexplored in terms of the directed information flow between brain regions. Method Sixteen participants observed the performance of four computer algorithms, which differed in credibility and reliability, of the system monitoring subtask of the Air Force Multi-Attribute Task Battery (AF-MATB). Using six brain regions of the CEN and DMN commonly identified to be activated in human trust, a total of 30 (forward, backward, and lateral) connection models were developed. Bayesian model averaging (BMA) was used to quantify the connectivity strength among the brain regions. Results Relative to the high trust condition, low trust showed unique presence of specific connections, greater connectivity strengths from the prefrontal cortex, and greater network complexity. High trust condition showed no backward connections. Conclusion Results indicated that trust and distrust can be two distinctive neural processes in human–automation interaction—distrust being a more complex network than trust, possibly due to the increased cognitive load. Application The causal architecture of distributed brain regions inferred using DCM can help not only in the design of a balanced human–automation interface design but also in the proper use of automation in real-life situations.

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Section: Influencing Factors Of Trustmentioning

confidence: 99%

Evaluating Effective Connectivity of Trust in Human–Automation Interaction: A Dynamic Causal Modeling (DCM) Study

et al. 2021

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“…Scene visibility was varied by changing the weather of the environment and had two levels: high visibility where the scene was sunny and low visibility where the scene was foggy with snow. Second, situational characteristics such as automation reliability can also substantially impact trust dynamics [40]. In particular, users gain more trust in systems that are reliable.…”

Section: Online Study Designmentioning

confidence: 99%

Clustering Human Trust Dynamics for Customized Real-time Prediction

Liu

Akash

Misu

et al. 2021

Preprint

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Trust calibration is necessary to ensure appropriate user acceptance in advanced automation technologies. A significant challenge to achieve trust calibration is to quantitatively estimate human trust in real-time. Although multiple trust models exist, these models have limited predictive performance partly due to individual differences in trust dynamics. A personalized model for each person can address this issue, but it requires a significant amount of data for each user. We present a methodology to develop customized model by clustering humans based on their trust dynamics. The clustering-based method addresses the individual differences in trust dynamics while requiring significantly less data than personalized model. We show that our clustering-based customized models not only outperform the general model based on entire population, but also outperform simple demographic factor-based customized models. Specifically, we propose that two models based on "confident" and "skeptical" group of participants, respectively, can represent the trust behavior of the population. The "confident" participants, as compared to the "skeptical" participants, have higher initial trust levels, lose trust slower when they encounter low reliability operations, and have higher trust levels during trust-repair after the low reliability operations. In summary, clustering-based customized models improve trust prediction performance for further trust calibration considerations.

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“…Further, the studies did not explore and assess various means of displaying uncertainty in a driving context. Indeed, other researchers proposed to expand the knowledge in this area by exploring different modalities (auditory and visual) and the implications of presenting uncertainty qualitatively, quantitatively, or representationally [24]. Nevertheless, the described shortcomings remain unaddressed and justify the need for more research in the field.…”

Section: Uncertainty Communication In the Automotive Domainmentioning

confidence: 99%