Convergence Across Behavioral and Self-report Measures Evaluating Individuals' Trust in an Autonomous Golf Cart

Jenna E. Cotter, Emily H. O’Hear, R. C. Smitherman, Addison B. Bright, N. Tenhundfeld, Jason Forsyth, N. Sprague, Samy El-Tawab
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引用次数: 2

Abstract

As automation is becoming more prevalent across everything from military and health care settings to everyday household items, it is necessary to understand the nature of human interactions with these systems. One critically important element of these interactions is user trust, as it can predict automated systems' safe and effective use. Past research has evaluated individuals' trust in automation through a host of different assessment techniques such as self-report, physiological, and behavioral measures. However, to date, there has been little evaluation of the convergence across these measures in a real-world environment. Convergence across measures is a useful tool in understanding the mechanisms by which a cognitive construct is impacted and providing greater confidence that any single measure is evaluating what it purports to measure. The present study used an autonomous golf cart that drove participants to different locations around the campus of James Madison University while a camera recorded them. In addition, participants were given the AICP-R and TOAST to evaluate their complacency potential and trust, respectively. Researchers coded videos for verification/checking behaviors (i.e., participants looked at or interacted with the GUI used to control the cart) and nervous behaviors (i.e., bracing, fidgeting, etc.). Additionally, environmental 'obstacles' such as pedestrians, food-delivery robots, and construction were also coded for by watching a front-facing camera. Results indicate a disconnect between the self-report and behavioral measures evaluating trust. However, there was a relationship between the coded nervous behaviors and verification behaviors and a relationship between those and the presence of obstacles. This lack of convergence across measures indicates a need for future research to understand whether this non-convergence represents shortcomings with the measures themselves, the existing definition of trust as a construct, or perhaps indicates that there is a nuance that can be afforded by some measures over another.
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在自动高尔夫球车中评估个人信任的行为和自我报告测量的收敛性
随着自动化在从军事和医疗保健环境到日常家居用品的各个领域变得越来越普遍,有必要了解人类与这些系统交互的本质。这些交互的一个至关重要的因素是用户信任,因为它可以预测自动化系统的安全和有效使用。过去的研究通过一系列不同的评估技术,如自我报告、生理和行为测量来评估个人对自动化的信任。然而,到目前为止,在现实环境中对这些措施的收敛性进行的评估很少。在理解认知结构受到影响的机制方面,跨度量的趋同是一个有用的工具,并提供了更大的信心,即任何单个度量都在评估它声称要测量的内容。目前的研究使用了一辆自动高尔夫球车,它将参与者带到詹姆斯麦迪逊大学校园内的不同地点,同时一台摄像机将他们记录下来。此外,参与者还分别获得了AICP-R和TOAST来评估他们的自满潜力和信任。研究人员将视频编码为验证/检查行为(即,参与者查看或与用于控制购物车的GUI交互)和紧张行为(即,支撑,坐立不安等)。此外,通过观察前置摄像头,行人、送餐机器人和建筑等环境“障碍”也会被编码。结果表明,自我报告和评估信任的行为措施之间存在脱节。然而,编码神经行为与验证行为之间存在一定的关系,而验证行为与障碍存在之间也存在一定的关系。这些措施之间缺乏趋同表明需要进行未来的研究,以了解这种不趋同是否代表了这些措施本身的缺点,现有的信任定义作为一种结构,或者可能表明一些措施可以提供另一个措施的细微差别。
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