A Comparison of Trust Measures in Human–Robot Interaction Scenarios

Kessler, Theresa T.; Larios, Cintya N.; Walker, Tiffani; Yerdon, Valarie; Hancock, Peter A.

doi:10.1007/978-3-319-41959-6_29

Cited by 17 publications

(8 citation statements)

References 24 publications

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“…We hypothesized that social conformity would exert a significant influence upon human-robot trust. Additionally, personal opinions of robots were expected to play a role in the development of trust, as suggested in a previous report (see Kessler, Larios, Walker, Yerdon, & Hancock, 2017).…”

Section: Social Conformity In Simulation-based Human-robot Interactionmentioning

confidence: 75%

Social Conformity Effects on Trust in Simulation-Based Human-Robot Interaction

Volante¹,

Sosna²,

Kessler

et al. 2018

Hum Factors

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Objective: We investigated the co-acting influences of communication and social conformity on trust in human-robot interaction. Background: Previous work has investigated aspects of the robot, the human, and the environment as influential factors in the human-robot relationship. Little work has examined the conjoint effects of social conformity and communication on this relationship. As social conformity and communication have been shown to affect human-human trust, there are a priori reasons to believe that they will play an influential role in human-robot trust also. Method: The experiment examined the influences of social conformity and robot communication on trust. A 2 × 2 (communication × social group) design was implemented with each variable having two levels (communication, no communication; positive social group, negative social group). Results: We created a communication manipulation which we then demonstrated to mediate the trust level between human and robot. However, this influence on trust was overcome by social information in which the subsequent trust level, attributed to the robot, was dominated by expressed social group attitudes to that robot. Conclusion: The results confirm the importance of human social assessments over direct robot communication in setting human-robot trust levels. When social opinions are expressed, observers appear to conform to the trust displayed by the group than relying on their own judgment. Application: In human-robot teams, the perceptions of the group may exert a greater impact than even robot communication. This may be especially important when new human members are introduced into such teams.

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Section: Social Conformity In Simulation-based Human-robot Interactionmentioning

confidence: 75%

Social Conformity Effects on Trust in Simulation-Based Human-Robot Interaction

Volante¹,

Sosna²,

Kessler

et al. 2018

Hum Factors

Self Cite

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show abstract

“…Within the increasing number of studies of human-robot interaction (HRI), there has been a great deal of focus on physical safety [28], teleoperation of robots [29][30][31], the quality of HRI [32], collaboration [33,34] and navigation [35][36][37]. More recently, in relation to social robots, there has been an increasing focus on empathy [38][39][40] and trust [41][42][43][44], important factors in developing a social robot. However, increasing our understanding of these important factors as they relate to HRI requires appropriate and contextually valid methods to inform research findings and robotic design.…”

Section: Human-robot Interaction Methodsmentioning

confidence: 99%

Participatory Research Principles in Human-Centered Design: Engaging Teens in the Co-Design of a Social Robot

Björling

Rose

2019

MTI

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Social robots are emerging as an important intervention for a variety of vulnerable populations. However, engaging participants in the design of social robots in a way that is ethical, meaningful, and rigorous can be challenging. Many current methods in human–robotic interaction rely on laboratory practices, often experimental, and many times involving deception which could erode trust in vulnerable populations. Therefore, in this paper, we share our human-centered design methodology informed by a participatory approach, drawing on three years of data from a project aimed to design and develop a social robot to improve the mental health of teens. We present three method cases from the project that describe creative and age appropriate methods to gather contextually valid data from a teen population. Specific techniques include design research, scenario and script writing, prototyping, and teens as operators and collaborative actors. In each case, we describe the method and its implementation and discuss the potential strengths and limitations. We conclude by situating these methods by presenting a set of recommended participatory research principles that may be appropriate for designing new technologies with vulnerable populations.

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“…Trust in automation is subjectively measured on scales from 1 to n points, where n can range from 5 to 100 (Kessler, Larios, Walker, Yerdon, & Hancock, 2017). Across the literature, trust has been treated as being at ordinal, interval (evidenced by the prolific use of parametric statistics), and ratio (evidenced by analysts computing percent changes in trust values; Lee and Moray 1992) levels.…”

Section: Background Measuring Trust In Automationmentioning

confidence: 99%

Subjective Measurement of Trust: Is It on the Level?

Wei

Bolton

Humphrey

2019

Proceedings of the Human Factors and Ergonomics Society Annual

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Psychometrics are increasingly being used to evaluate trust in the automation of safety-critical systems. There is no consensus on what the highest level of measurement is for psychometric trust. This is important as the level of measurement determines what mathematics and statistics can be meaningfully applied to ratings. In this work, we introduce a new method for determining what the maximum level of measurement is for psychometric ratings. We use this to assess the level of measurement of trust in automation using human ratings about the behavior of unmanned aerial systems performing search tasks. Results show that trust is best represented at an ordinal level and that it can be treated as interval in most situations. It is unlikely that trust in automation ratings are ratio. We discuss these results, their implications, and future research.

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A Comparison of Trust Measures in Human–Robot Interaction Scenarios

Cited by 17 publications

References 24 publications

Social Conformity Effects on Trust in Simulation-Based Human-Robot Interaction

Social Conformity Effects on Trust in Simulation-Based Human-Robot Interaction

Participatory Research Principles in Human-Centered Design: Engaging Teens in the Co-Design of a Social Robot

Subjective Measurement of Trust: Is It on the Level?

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