Ergonomics in Design: The Quarterly of Human Factors Applications is intended to serve the needs of practicing human factors/ergonomics professionals who are concerned with the usability of products, systems, tools, and environments. It provides up-to-date demonstrations of the importance of HF/E principles in design and implementation. Articles, case studies, anecdotes, debates, and interviews focus on the way in which HF/E research and methods are applied in the design, development, prototyping, test and evaluation, training, and manufacturing processes of a product or system.

Ergonomics in Design: The Quarterly of Human Factors Applications is dedicated to serving the needs of practicing human factors/ergonomics professionals concerned with the usability of products, systems, tools, and environments. The journal provides up-to-date demonstrations of the significance of HF/E principles in design and implementation. Articles, case studies, anecdotes, debates, and interviews focus on how HF/E research and methods are applied in the design, development, prototyping, testing and evaluation, training, and manufacturing processes of a product or system.

《Ergonomics in Design》是一本专注于人因工程应用的季刊，旨在满足关注产品、系统、工具和环境可用性的人因工程专业人士的需求。期刊提供了人因工程原则在设计和实施中的重要性最新示范。文章、案例研究、轶事、辩论和访谈聚焦于人因工程研究和方法在产品或系统的设计、开发、原型制作、测试与评估、培训和制造过程中的应用。

Ergonomics in Design

Uncrewed aircraft system (UAS) operators often monitor multiple channels of information, such as radio communications and tracking visual targets. We investigated how exposing pilots to divided attention tasks during flights impacted performance and their improvement with practice. Pilots completed a UAS course combined with visual and auditory cognitive tasks over several sessions. We found that flight efficiency and performance on the cognitive tasks decreased under divided attention. Flight performance also improved over the course of the sessions, including in conditions of divided visual attention. The tested procedure is promising for training purposes in which operators will encounter high cognitive demands.

Collin Scarince

Miguel Moreno

Madison Clausen

Tye Payne

Ergonomics in Design The Quarterly of Human Factors Applications

Training UAS Pilots for Divided Attention Demands With Cognitive Tasks and NIST Courses

使用认知任务和NIST课程训练UAS飞行员应对分心需求

This short feature article recommends learning from the M&amp;S community and shifting from trust in the model towards trust in the result. Trust is not only gained by technically mature solutions but is predominantly shaped by the relationship between provider and customer.

Andreas Tolk

Can We Learn From Trust in Simulation to Gain Trust in AI?

我们能否从模拟中的信任学习以获得对人工智能的信任？

Trust is fundamental to creating a lasting relationship with another human being. In our daily lives, we encounter several situations where we place trust on other humans such as bus drivers, colleagues, and even strangers. Trust in human-human teams is initially founded on the predictability of the trustee, and as the relationship between the trustor and the trustee progresses, dependability or integrity replaces predictability as the basis of trust (Hoff & Bashir, 2015). As artificial intelligence (AI) gets smarter and smarter, it is becoming an integral part of human lives. For example, AI is being used for scheduling appointments, powering smart homes, recognizing people’s faces in photos, making health diagnoses, providing investment advice, and several other things. As with a humanhuman team, trust is a necessary ingredient for human-AI partnerships. However, trust in human-human teams progresses in the reverse order from human-human teams (e.g., Hoff & Bashir, 2015). This is because humans initially assume that AI is near perfect. Therefore, in the initial stages, faith forms the essential constituent of trust, and as the number of exchanges between the human and the AI increases, faith is replaced by dependability and predictability. Trust in AI is considered a twodimensional construct comprising trust and distrust, where trust is associated with feelings of calmness and security and distrust involves fear and worry (Lyons, Stokes, Eschleman, Alarcon, & Barelka, 2011). Unarguably, trust is a complex social process with a variety of factors determining the extent to which humans trust AI agents (Hoff & Bashir, 2015; Lee & See, 2004). Specifically, in order to calibrate the right level of trust in AI, consider dispositional (i.e., user characteristics such as age, culture, gender, and personality), internal (e.g., user characteristics such as workload, mood, self-confidence, and working memory capacity), environmental (e.g., task difficulty, perceived risks and benefits, organizational setting), and learned factors (e.g., reputation of the AI, reliability, consistency, type and timing of errors made by the AI, and users’ experience with similar agents). Design factors (e.g., appearance, ease of use, communication style, and transparency of the AI) also affect perceptions of trust. For example, anthrophomorphizing is an effective way of establishing a long-term social bond between humans and AI agents, which is driven by the neurotransmitter and hormone, oxytocin (e.g., de Visser et al., 2017). Further, anthropomorphic agents also resist breakdown in trust compared to their counterpart non-anthropomorphic agents, presumably because anthropomorphic agents remind users of humans who are forgiven more easily for being imperfect in comparison to machine-like agents. Interestingly, there is also evidence that humans tend to disclose more information to AI therapists than human therapists. Transparency of the AI also helps to calibrate the right level of trust by enabling users to develop accurate mental models of the AI underpinnings (e.g., Balfe, Sharples, & Wilson, 2018). 818592 ERGXXX10.1177/1064804618818592ergonomics in designergonomics in design research-article2018

Arathi Sethumadhavan

Trust in Artificial Intelligence

人工智能信任

This field study reports on a wireless, wearable, automated voice-directed system where truck loaders at a distribution center wear a headset to listen to instructions and speak into a microphone to load pallets of product into delivery trucks accurately. The system was designed to provide immediate and corrective feedback to load pallets in reverse-stop sequence for all multi-stop routes. The voice-directed system intervention was instrumental in reducing the number of pallets loaded out of sequence to 0.015%, based on approximately 175 weekly routes, amounting to a decrease of an out-of-sequence percentage error by 99%.

David T. Goomas

Ergonomic Improvement at a Distribution Center Using a Voice-Directed Feedback System to Manage Loading Trucks

配送中心使用语音导向反馈系统管理装载卡车的工效学改进

The quest for an adequate model of trust in AI systems has led to rosters of influencing factors and proposals for how trust can be “designed-in.” An alternative is to consider trusting as an emergent. While progressive maturation of user trust may be desired, it is the exception. Trust can arise rapidly, but it can diminish in a heartbeat. It can be orthogonal to reliance. Trusting and relying are context-contingent. Work-centered design principles can make trustability achievable, but it can only be realized through user experience. And principles for “designing-in” trust do not span the creativity gap.

Abbreviation	Ergonomics in Design
Journal Impact	0.79
Quartiles(Global)	ERGONOMICS(Q4)

ISSN	1064-8046, 2169-5083
h-index	26

Publication Information	Publisher: SAGE Publications Inc.，Publishing cycle: ，Journal Type: journal，Open Access Journals: No
Basic data	Year of publication: 1993，Proportion of original research papers: 100.00%，Self Citation Rate:0.00%， Gold OA Rate: 4.92%

Ergonomics in Design

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Training UAS Pilots for Divided Attention Demands With Cognitive Tasks and NIST Courses

Can We Learn From Trust in Simulation to Gain Trust in AI?

Trust in Artificial Intelligence

Ergonomic Improvement at a Distribution Center Using a Voice-Directed Feedback System to Manage Loading Trucks

Trusting as an Emergent: Implications for Design

Training UAS Pilots for Divided Attention Demands With Cognitive Tasks and NIST Courses

Can We Learn From Trust in Simulation to Gain Trust in AI?

Trusting as an Emergent: Implications for Design

Comment From the Editor-In-Chief

Ergonomic Improvement at a Distribution Center Using a Voice-Directed Feedback System to Manage Loading Trucks