Objective:We systematically review recent empirical research on factors that influence trust in automation to present a three-layered trust model that synthesizes existing knowledge.Background:Much...

Trust in Automation: Integrating Empirical Evidence on Factors That Influence Trust

Objective: We evaluate and quantify the effects of human, robot, and environmental factors on perceived trust in human-robot interaction (HRI)Background: To date, reviews of trust in HRI have been

/pdf/a-meta-analysis-of-factors-affecting-trust-in-human-robot-1305er4ix4.pdf

A meta-analysis of factors affecting trust in human-robot interaction.

Automation bias: a systematic review of frequency, effect mediators, and mitigators.

OBJECTIVE: The feasibility of measuring drivers' automation trust via gaze behavior during highly automated driving was assessed with eye tracking and validated with self-reported automation trust in a driving simulator study. BACKGROUND: Earlier research from other domains indicates that drivers' automation trust might be inferred from gaze behavior, such as monitoring frequency. METHOD: The gaze behavior and self-reported automation trust of 35 participants attending to a visually demanding non-driving-related task (NDRT) during highly automated driving was evaluated. The relationship between dispositional, situational, and learned automation trust with gaze behavior was compared. RESULTS: Overall, there was a consistent relationship between drivers' automation trust and gaze behavior. Participants reporting higher automation trust tended to monitor the automation less frequently. Further analyses revealed that higher automation trust was associated with lower monitoring frequency of the automation during NDRTs, and an increase in trust over the experimental session was connected with a decrease in monitoring frequency. CONCLUSION: We suggest that (a) the current results indicate a negative relationship between drivers' self-reported automation trust and monitoring frequency, (b) gaze behavior provides a more direct measure of automation trust than other behavioral measures, and (c) with further refinement, drivers' automation trust during highly automated driving might be inferred from gaze behavior. APPLICATION: Potential applications of this research include the estimation of drivers' automation trust and reliance during highly automated driving. Keywords: Driver distraction Language: en

Keep Your Scanners Peeled: Gaze Behavior as a Measure of Automation Trust During Highly Automated Driving

As robots become increasingly common in a wide variety of domains—from military and scientific applications to entertainment and home use—there is an increasing need to define and assess the trust humans have when interacting with robots. In human interaction with robots and automation, previous work has discovered that humans often have a tendency to either overuse automation, especially in cases of high workload, or underuse automation, both of which can make negative outcomes more likely. Frthermore, this is not limited to naive users, but experienced ones as well. Robotics brings a new dimension to previous work in trust in automation, as they are envisioned by many to work as teammates with their operators in increasingly complex tasks. In this chapter, our goal is to highlight previous work in trust in automation and human-robot interaction and draw conclusions and recommendations based on the existing literature. We believe that, while significant progress has been made in recent years, especially in quantifying and modeling trust, there are still several places where more investigation is needed.

The role of trust in human-robot interaction

The purpose of this research was to investigate the effects that user task load level has on the relationship between an individual's trust in and subsequent use of a system's automation. Military decision-makers trust and use information system automation to make many tactical judgments and decisions. In situations of information uncertainty (information warfare environments), decision-makers must remain aware of information reliability issues and temperate their use of system automation if necessary. An individual's task load may have an effect on his use of a system's automation in environments of information uncertainty. It was hypothesized that user task load will have a moderating effect on the positive relationship between system automation trust and use of system automation. Specifically, in situations of information uncertainty (low trust), high task load will have a negative effect on the relationship. To test this hypothesis, an experiment in a simulated command and control micro-world was conducted in which system automation trust and individual task load were manipulated. The findings from the experiment support the positive relationship between automation trust and automation use found in previous research and suggest that task load does have a negative effect on the positive relationship between automation trust and automation use. Experiment participant who incurred a higher task load exhibited an over-reliance on their automated information systems to assist them in their decision-making activities. Such an over-reliance can lead to vulnerabilities of deception and suggests the need for automated deception detection capabilities.

The Influence of Task Load and Automation Trust on Deception Detection

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The Influence of Task Load and Automation Trust on Deception Detection

High heritability of ascending aortic diameter and trans-ancestry prediction of thoracic aortic disease