首个可量化的机器人危险感知量表，用于评估人机交互中的安全感受。
The Perceived Danger (PD) Scale: Development and Validation
- 基于四次研究构建12项双因子量表，涵盖情绪、脆弱性等四维度。
- 量表预测能力优于现有安全感知量表，且对机器人速度变化敏感。
- 适用于人机交互研究，尤其关注用户对机器人危险感的量化分析。
目前缺乏心理测量学有效的机器人危险感知评估工具。为填补这一空白，本文定义了危险感知，并通过四项研究开发并验证了一个12项的双因子量表。探索性因素分析揭示了四个子维度：情感状态、身体脆弱性、诡异感和认知准备度。验证性因素分析确认了双因子模型。将该量表与Godspeed感知安全量表比较后发现，其对实证数据的预测能力更优。在实地实验中进一步验证，该量表能敏感反映机器人速度变化带来的感知危险差异，与以往研究结果一致。多实验结果表明，该量表具有良好的信度、效度，是人机交互情境下感知危险与安全的有效预测工具。
原文摘要 · Abstract (English)
There are currently no psychometrically valid tools to measure the perceived danger of robots. To fill this gap, we provided a definition of perceived danger and developed and validated a 12-item bifactor scale through four studies. An exploratory factor analysis revealed four subdimensions of perceived danger: affective states, physical vulnerability, ominousness, and cognitive readiness. A confirmatory factor analysis confirmed the bifactor model. We then compared the perceived danger scale to the Godspeed perceived safety scale and found that the perceived danger scale is a better predictor of empirical data. We also validated the scale in an in-person setting and found that the perceived danger scale is sensitive to robot speed manipulations, consistent with previous empirical findings. Results across experiments suggest that the perceived danger scale is reliable, valid, and an adequate predictor of both perceived safety and perceived danger in human-robot interaction contexts.
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