Helping Not Hurting: Applying the Stereotype Content Model and BIAS Map to Social Robotics

Mieczkowski, Hannah; Liu, Sunny Xun; Hancock, Jeffrey T.; Reeves, Byron

doi:10.1109/hri.2019.8673307

Cited by 35 publications

(20 citation statements)

References 29 publications

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“…As people have shown the same tendency to form impressions for robots as they do for humans [21], our findings may be explained by what we know about human impression formation. From social psychology, it is known that warmth and competence are the primary focus in impression formation among people.…”

Section: Discussionsupporting

confidence: 60%

“…As many people interacting with robots will not have prior experience, they are likely to intuitively form their impressions of a robot based on its outer appearance during the first milliseconds of interaction, as is typical among humans [31]. In fact, people ascribe social attributes, such as warmth and competence, to social robots based on their physical characteristics [21].…”

Section: Introductionmentioning

confidence: 99%

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Knock on Wood: The Effects of Material Choice on the Perception of Social Robots

et al. 2019

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Many people who interact with robots in the near future will not have prior experience, and they are likely to intuitively form their first impressions of the robot based on its appearance. This paper explores the effects of component material on people's perception of the robots in terms of social attributes and willingness to interact. Participants watched videos of three robots with different outer materials: wood, synthetic fur, and plastic. The results showed that people rated the perceived warmth of a plastic robot lower than a wooden or furry robot. Ratings of perceived competence and discomfort did not differ between the three robots.

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Section: Discussionsupporting

confidence: 60%

Section: Introductionmentioning

confidence: 99%

Knock on Wood: The Effects of Material Choice on the Perception of Social Robots

et al. 2019

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“…For example, high competence and low warmth can elicit envy and cause harming actions, while low competence and high warmth can elicit pity and cause helping actions. Such mappings between impressions, emotional responses, and behavioral tendencies have been observed in human interactions with virtual agents [121] and robots [114]. Moreover, first impressions established during short-term HRI were found to persist in repeated interaction sessions [130].…”

Section: Discussionmentioning

confidence: 68%

A Taxonomy of Social Errors in Human-Robot Interaction

Tian

OviattSharon

2021

J. Hum.-Robot Interact.

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Robotic applications have entered various aspects of our lives, such as health care and educational services. In such Human-robot Interaction (HRI), trust and mutual adaption are established and maintained through a positive social relationship between a user and a robot. This social relationship relies on the perceived competence of a robot on the social-emotional dimension. However, because of technical limitations and user heterogeneity, current HRI is far from error-free, especially when a system leaves controlled lab environments and is applied to in-the-wild conditions. Errors in HRI may either degrade a user’s perception of a robot’s capability in achieving a task (defined as performance errors in this work) or degrade a user’s perception of a robot’s socio-affective competence (defined as social errors in this work). The impact of these errors and effective strategies to handle such an impact remains an open question. We focus on social errors in HRI in this work. In particular, we identify the major attributes of perceived socio-affective competence by reviewing human social interaction studies and HRI error studies. This motivates us to propose a taxonomy of social errors in HRI. We then discuss the impact of social errors situated in three representative HRI scenarios. This article provides foundations for a systematic analysis of the social-emotional dimension of HRI. The proposed taxonomy of social errors encourages the development of user-centered HRI systems, designed to offer positive and adaptive interaction experiences and improved interaction outcomes.

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“…此外, 人与机器人交互之中的一些具体行为也会对接受度产生影响. 例如, 研究发现参与机器人的制作 [96] 或者组装过程 [97] [99] , 它能够预测使用者特定的情绪反应(如钦佩、嫉妒、轻蔑和怜悯等), 并进而预测特定的行为倾向(帮助或伤害) [100] . 对机器人社会存在的感知也是影响机器人接受度的一个重要因素.…”

Section: 拟人化是在机器人外观研究中最为热点的一个问题unclassified

“…当我们在对机器人进行人类知觉时, 大多数情况下我们在进行拟人化过程 [20] . [127] 判断其年龄、性别甚至其种族 [128] , 同时还会将适用于人类的社会规则赋予机器人, 如性别角色 [42,129,130] 、种族偏见 [100] 等. 这些反应不仅体现在成人身上, 在儿童身上也已有体现 [53] , 在识别拟人化机器人时, 类似人类面孔识别的面孔倒置效应都同样会出现 [131] .…”

Section: 拟人化是机器人接受度的核心unclassified

Factors that influence robot acceptance

Xu¹,

Ye²

2019

Chin. Sci. Bull.

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Helping Not Hurting: Applying the Stereotype Content Model and BIAS Map to Social Robotics

Cited by 35 publications

References 29 publications

Knock on Wood: The Effects of Material Choice on the Perception of Social Robots

Knock on Wood: The Effects of Material Choice on the Perception of Social Robots

A Taxonomy of Social Errors in Human-Robot Interaction

Factors that influence robot acceptance

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