Evaluating effects of workload on trust in automation, attention allocation and dual-task performance

Zhang, Meng Yuan; Yang, X. Jessie

doi:10.1177/1541931213601932

Cited by 6 publications

(1 citation statement)

References 6 publications

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“…The majority of prior literature adopted a snapshot view of trust and typically evaluated trust through questionnaires administered at the end of an experiment. More than two dozen factors have been identified to influence one’s (snapshot) trust in automation, including individual factors such as culture and age (McBride et al, 2011; Rau et al, 2009), system factors such as reliability and level of automation (Du et al, 2020; Parasuraman et al, 2000; Wickens & Dixon, 2007; Wickens et al, 2009), and environmental factors such as multitasking requirement (Zhang & Yang, 2017). This snapshot view, however, does not fully acknowledge that trust is a dynamic variable that can change over time (Figure 1).…”

Section: Introductionmentioning

confidence: 99%

Toward Quantifying Trust Dynamics: How People Adjust Their Trust After Moment-to-Moment Interaction With Automation

2021

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Objective We examine how human operators adjust their trust in automation as a result of their moment-to-moment interaction with automation. Background Most existing studies measured trust by administering questionnaires at the end of an experiment. Only a limited number of studies viewed trust as a dynamic variable that can strengthen or decay over time. Method Seventy-five participants took part in an aided memory recognition task. In the task, participants viewed a series of images and later on performed 40 trials of the recognition task to identify a target image when it was presented with a distractor. In each trial, participants performed the initial recognition by themselves, received a recommendation from an automated decision aid, and performed the final recognition. After each trial, participants reported their trust on a visual analog scale. Results Outcome bias and contrast effect significantly influence human operators’ trust adjustments. An automation failure leads to a larger trust decrement if the final outcome is undesirable, and a marginally larger trust decrement if the human operator succeeds the task by him/herself. An automation success engenders a greater trust increment if the human operator fails the task. Additionally, automation failures have a larger effect on trust adjustment than automation successes. Conclusion Human operators adjust their trust in automation as a result of their moment-to-moment interaction with automation. Their trust adjustments are significantly influenced by decision-making heuristics/biases. Application Understanding the trust adjustment process enables accurate prediction of the operators’ moment-to-moment trust in automation and informs the design of trust-aware adaptive automation.

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Section: Introductionmentioning

confidence: 99%

Toward Quantifying Trust Dynamics: How People Adjust Their Trust After Moment-to-Moment Interaction With Automation

2021

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Modeling and Predicting Trust Dynamics in Human–Robot Teaming: A Bayesian Inference Approach

Guo

Yang

2020

Int J of Soc Robotics

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Trust in automation, or more recently trust in autonomy, has received extensive research attention in the past three decades. The majority of prior literature adopted a “snapshot” view of trust and typically evaluated trust through questionnaires administered at the end of an experiment. This “snapshot” view, however, does not acknowledge that trust is a dynamic variable that can strengthen or decay over time. To fill the research gap, the present study aims to model trust dynamics when a human interacts with a robotic agent over time. The underlying premise of the study is that by interacting with a robotic agent and observing its performance over time, a rational human agent will update his/her trust in the robotic agent accordingly. Based on this premise, we develop a personalized trust prediction model and learn its parameters using Bayesian inference. Our proposed model adheres to three properties of trust dynamics characterizing human agents’ trust development process de facto and thus guarantees high model explicability and generalizability. We tested the proposed method using an existing dataset involving 39 human participants interacting with four drones in a simulated surveillance mission. The proposed method obtained a root mean square error of 0.072, significantly outperforming existing prediction methods. Moreover, we identified three distinct types of trust dynamics, the Bayesian decision maker, the oscillator, and the disbeliever, respectively. This prediction model can be used for the design of individualized and adaptive technologies.

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Modeling Trust Dynamics in Human-robot Teaming: A Bayesian Inference Approach

Guo

Zhang

Yang

2020

Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems

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Trust in automation, or more recently trust in autonomy, has received extensive research attention in the past two decades. The majority of prior literature adopted a "snapshot" view of trust and typically evaluated trust through questionnaires administered at the end of an experiment. This "snapshot" view, however, does not acknowledge that trust is a time-variant variable that can strengthen or decay over time. To fill the research gap, the present study aims to model trust dynamics when a human interacts with a robotic agent over time. The underlying premise of the study is that by interacting with a robotic agent and observing its performance over time, a rational human agent will update his/her trust in the robotic agent accordingly. Based on this premise, we develop a personalized trust prediction model based on Beta distribution and learn its parameters using Bayesian inference. Our proposed model adheres to three major properties of trust dynamics reported in prior empirical studies. We tested the proposed method using an existing dataset involving 39 human participants interacting with four drones in a simulated surveillance mission. The proposed method obtained a Root Mean Square Error (RMSE) of 0.072, significantly outperforming existing prediction methods. Moreover, we identified three distinctive types of trust dynamics, the Bayesian decision maker, the oscillator, and the disbeliever, respec

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Evaluating effects of workload on trust in automation, attention allocation and dual-task performance

Cited by 6 publications

References 6 publications

Toward Quantifying Trust Dynamics: How People Adjust Their Trust After Moment-to-Moment Interaction With Automation

Toward Quantifying Trust Dynamics: How People Adjust Their Trust After Moment-to-Moment Interaction With Automation

Modeling and Predicting Trust Dynamics in Human–Robot Teaming: A Bayesian Inference Approach

Modeling Trust Dynamics in Human-robot Teaming: A Bayesian Inference Approach

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