Collaborative Research: Animated Agents in Self-Administered Surveys

这个项目考察了动画或对话代理技术对基于网络的调查的影响。动画代理是用户界面中的拟人化软件对象，在他们最先进的实现中，它们产生手势，显示面部表情，并与他们的语音协调移动他们的眼睛。它们已被证明可以提高用户在某些任务领域的表现(例如辅导)。这项技术何时可能有助于或损害调查数据质量和受访者的满意度？代理人必须有多老练才能提供好处--或者是伤害？在网络调查中，代理可以激励受访者参与并完成任务，就像人类面试者所做的那样，并帮助受访者按预期理解调查问题，同时允许受访者在方便的时候参与，就像在普通的网络调查中一样。另一方面，特工的存在可能会阻碍人们诚实回答有关敏感话题的问题，就像人类面试官被证明会做的那样。在这个项目中，一系列的实验室实验检测了越来越少的类似人类的代理人，这些代理人询问受访者的敏感和非敏感行为。这项研究将非代理网络调查的数据质量和用户满意度与具有界面代理的网络调查进行了对比，界面代理的对话能力、提供关于其内部状态的视觉和语音提示的程度以及讲话中的意图程度都不同。这些特工是用软件模拟的，该软件可以将现场面试官的视频图像实时转换为动画；因此，受访者认为他们是在与计算机生成的特工互动，即使在这名特工背后实际上有一个人。在关于非敏感行为的实验中，受试者根据虚构的情景进行回答，以确定他们回答的准确性。一个可能的结果是，具有更大对话能力的代理人将促进互动，从而导致更准确的理解和更准确的答案。在关于敏感行为的实验中，受访者回答了自己的生活；关于敏感行为的报告越多，表明受访者越坦率。一个可能的结果是，活动更多的代理(嘴唇、眼睛和眉毛)会导致受访者感觉不那么私密，因此与活动有限的代理相比，回答不那么坦率。受访者的满意度是通过访谈后问卷来衡量的；不同的代理功能对受访者与调查系统的沟通方式的影响是通过对所有对话的详细逐次编码来衡量的。这项工作的实际影响将是调查研究人员在采用代理技术时做出更明智的决策。了解代理何时提供帮助以及哪些功能最有帮助，可以集中决定开发哪些接口，不开发哪些接口。例如，如果对话能力对数据质量和用户满意度比其他代理功能更重要，这可能会使未来的开发努力更多地集中在代理的会话能力上，而不是视觉真实感上。拟议工作的理论影响将体现在两个方面。首先，它将加深我们对语言和非语言交流如何相互联系的理解，例如，语言互动如何受到代理人面部展示保真度的影响。其次，通过比较人机交互和人与人之间的交互，该项目将促进对意向性归因和人类中介如何更广泛地影响交互的了解。作为支持调查和统计方法研究的联合活动的一部分，这项研究得到了方法学、测量和统计计划和一个联邦统计机构联盟的支持。

This project examines the impact of animated or conversational agent technology on web-based surveys. Animated agents are anthropomorphic software objects in the user interface that, in their most advanced implementations, produce gestures, display facial expressions, and move their eyes in coordination with their speech. They have been shown to improve user performance in some task domains (e.g. tutoring). When might this technology help or hurt survey data quality and respondents' satisfaction? How sophisticated must the agents be in order to provide benefit--or harm? In a web survey, an agent might motivate respondents to participate and complete the task, much like human interviewers do, and help respondents understand the survey questions as intended while allowing respondents to participate at their convenience, as in ordinary web surveys. On the other hand, the presence of an agent might discourage honest responding to questions about sensitive topics, much as human interviewers have been shown to do. In this project a series of laboratory experiments examine more and less human-like agents that ask questions about respondents' sensitive and non-sensitive behaviors. The studies contrast data quality and user satisfaction in non-agent web surveys to those with interface agents that vary in their dialogue capability, the degree to which they provide visual and spoken cues about their internal states, and the degree of intentionality in their speech. The agents are simulated with software that converts a video image of a live interviewer into an animation in real time; respondents thus believe they are interacting with a computer-generated agent even though there is actually a human behind the "agent." In the experiments about non-sensitive behaviors, respondents answer on the basis of fictional scenarios so that the accuracy of their answers can be determined. One possible outcome is that agents with greater dialogue capability will promote interactions that lead to more accurate understanding and thus more accurate answers. In the experiments about sensitive behaviors, respondents answer about their own lives; more reports of sensitive behaviors indicate greater respondent candor. One possible outcome is that agents with more movement (lips, eyes, and eyebrows) will lead respondents to feel less private and therefore to answer less candidly than with agents whose movement is limited. Respondents' satisfaction is measured with a post-interview questionnaire; the impact of different agent features on how respondents communicate with the survey system is measured by detailed turn-by-turn coding of all dialogue.The practical impact of this work will be more informed decisions by survey researchers in adopting agent technology. Knowing when agents help and what features help the most can focus decisions about what interfaces to develop and which ones not to develop. For example, if dialogue capability is more important to data quality and user satisfaction than other agent features, this could focus future development efforts on conversational competence of agents more than on visual realism. The theoretical impact of the proposed work will be in two areas. First, it will deepen our understanding of how verbal and non-verbal communication are interconnected, for example how verbal interaction is affected by the fidelity of the agents' facial display. Second, by comparing human-computer and human-human interaction the project will advance knowledge of how attributions of intentionality and human agency affect interaction more generally. This research is supported by the Methodology, Measurement, and Statistics Program and a consortium of federal statistical agencies as part of a joint activity to support research on survey and statistical methodology.