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相关概念视频

Stereotype Content Model02:16

Stereotype Content Model

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The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
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相关实验视频

Updated: Jul 24, 2025

Investigating Pain-Related Avoidance Behavior using a Robotic Arm-Reaching Paradigm
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人与机器人触觉交互中的风险承担行为模型.

Qiaoqiao Ren1, Yuanbo Hou2, Dick Botteldooren2

  • 1AIRO-IDLab, Faculty of Engineering and Architecture, Ghent University-Imec, Technologiepark 126, 9052 Gent, Belgium.

Sensors (Basel, Switzerland)
|July 11, 2023
PubMed
概括
此摘要是机器生成的。

社交机器人的触摸强度影响了人类的风险承担. 生理反应和触觉交互强度可以预测人机交互期间的冒险行为,提高预测准确度.

关键词:
这是一个行为模型.人与机器人的触觉互动非语言互动是一种非语言互动.承担风险的行为.

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科学领域:

  • 人与机器人的交互
  • 情感计算是一种情感计算.
  • 物理计算的物理计算.

背景情况:

  • 触觉互动显著影响人类行为和社会动态.
  • 以前的研究表明,机器人的触觉强度会影响人类的冒险倾向.
  • 了解这种互动的生理基础对于设计有效的社交机器人至关重要.

研究的目的:

  • 研究人类冒险行为,生理反应和触觉互动强度与社交机器人之间的关系.
  • 开发和评估机器学习模型,用于预测人机触觉交互期间的冒险行为.
  • 在人机交互中识别风险处理的关键生理和行为指标.

主要方法:

  • 从参与者收集的生理传感器数据在人机器人触觉交互期间玩气球模拟风险任务 (BART).
  • 利用混合效应模型作为基线,从生理测量中预测风险承担倾向.
  • 采用机器学习技术,特别是支持向量回归 (SVR) 和多输入卷积多头注意力 (MCMA),以提高预测准确度和延迟.

主要成果:

  • 在预测风险行为方面,MCMA模型显著优于基线混合效应模型.
  • MCMA的平均绝对误差 (MAE) 为3.17,根平均平方误差 (RMSE) 为4.38,R平方 (R2) 为0.93,相比之下,基线的MAE为10.97,RMSE为14.73和R2为0.30.
  • 生理激活和触觉交互强度被确定为人机交互期间风险处理的突出因素.

结论:

  • 生理学数据与行为和触觉交互数据相结合,可以准确地预测人类在人机交互中的冒险行为.
  • 这项研究表明,使用机器学习来低延迟预测冒险行为的可行性.
  • 这些发现提供了关于生理反应,触觉交互和风险处理之间的相互作用的见解,为设计更直观和响应敏捷的社交机器人提供了信息.