Meaningful Communication but not Superficial Anthropomorphism Facilitates Human-Automation Trust Calibration: The Human-Automation Trust Expectation Model (HATEM).

IF 2.9 3区 心理学 Q1 BEHAVIORAL SCIENCES
Human Factors Pub Date : 2024-11-01 Epub Date: 2023-12-02 DOI:10.1177/00187208231218156
Owen B J Carter, Shayne Loft, Troy A W Visser
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引用次数: 0

Abstract

Objective: The objective was to demonstrate anthropomorphism needs to communicate contextually useful information to increase user confidence and accurately calibrate human trust in automation.

Background: Anthropomorphism is believed to improve human-automation trust but supporting evidence remains equivocal. We test the Human-Automation Trust Expectation Model (HATEM) that predicts improvements to trust calibration and confidence in accepted advice arising from anthropomorphism will be weak unless it aids naturalistic communication of contextually useful information to facilitate prediction of automation failures.

Method: Ninety-eight undergraduates used a submarine periscope simulator to classify ships, aided by the Ship Automated Modelling (SAM) system that was 50% reliable. A between-subjects 2 × 3 design compared SAM appearance (anthropomorphic avatar vs. camera eye) and voice inflection (monotone vs. meaningless vs. meaningful), with the meaningful inflections communicating contextually useful information about automated advice regarding certainty and uncertainty.

Results: Avatar SAM appearance was rated as more anthropomorphic than camera eye, and meaningless and meaningful inflections were both rated more anthropomorphic than monotone. However, for subjective trust, trust calibration, and confidence in accepting SAM advice, there was no evidence of anthropomorphic appearance having any impact, while there was decisive evidence that meaningful inflections yielded better outcomes on these trust measures than monotone and meaningless inflections.

Conclusion: Anthropomorphism had negligible impact on human-automation trust unless its execution enhanced communication of relevant information that allowed participants to better calibrate expectations of automation performance.

Application: Designers using anthropomorphism to calibrate trust need to consider what contextually useful information will be communicated via anthropomorphic features.

有意义的沟通而非肤浅的拟人化促进人-自动化信任校准:人-自动化信任期望模型(HATEM)。
目的:目的是证明拟人化需要传达上下文有用的信息,以增加用户信心并准确校准人类对自动化的信任。背景:拟人化被认为可以提高人类对自动化的信任,但支持的证据仍然模棱两可。我们测试了人类-自动化信任期望模型(HATEM),该模型预测,除非拟人化有助于自然地交流上下文有用的信息,以促进自动化故障的预测,否则信任校准和对可接受建议的信心的改善将很弱。方法:98名大学生使用潜艇潜望镜模拟器对舰船进行分类,辅以舰船自动建模(SAM)系统,SAM系统的可靠性为50%。受试者之间的2 × 3设计比较了SAM外观(拟人化化身vs摄像机眼睛)和语音变化(单调vs无意义vs有意义),有意义的变化传达了关于确定性和不确定性的自动化建议的上下文有用信息。结果:Avatar SAM外观被评为比camera eye更拟人化,无意义和有意义的屈折都被评为比单调更拟人化。然而,对于主观信任、信任校准和接受SAM建议的信心,没有证据表明拟人化外观有任何影响,而有决定性的证据表明,有意义的屈折在这些信任措施上比单调和无意义的屈折产生更好的结果。结论:拟人化对人类-自动化信任的影响可以忽略不计,除非拟人化的执行增强了相关信息的沟通,使参与者能够更好地校准自动化性能的期望。应用:使用拟人化来校准信任的设计师需要考虑通过拟人化特征将传达哪些上下文有用的信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Human Factors
Human Factors 管理科学-行为科学
CiteScore
10.60
自引率
6.10%
发文量
99
审稿时长
6-12 weeks
期刊介绍: Human Factors: The Journal of the Human Factors and Ergonomics Society publishes peer-reviewed scientific studies in human factors/ergonomics that present theoretical and practical advances concerning the relationship between people and technologies, tools, environments, and systems. Papers published in Human Factors leverage fundamental knowledge of human capabilities and limitations – and the basic understanding of cognitive, physical, behavioral, physiological, social, developmental, affective, and motivational aspects of human performance – to yield design principles; enhance training, selection, and communication; and ultimately improve human-system interfaces and sociotechnical systems that lead to safer and more effective outcomes.
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