Jove
Visualize
联系我们

相关概念视频

Automatic Processing and Automatic Social Behavior01:28

Automatic Processing and Automatic Social Behavior

Automatic processing refers to the cognitive operations that occur without conscious intent or awareness, playing a fundamental role in shaping social cognition and behavior. These processes enable individuals to navigate complex social environments efficiently by relying on mental shortcuts and pre-existing knowledge structures known as schemas. One of the most influential mechanisms underlying automatic processing is priming, which subtly activates mental representations through exposure to...

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Legal and Ethical Considerations for Translating Federated Learning into Cross-Border Healthcare Innovation.

IEEE journal of biomedical and health informatics·2026
Same author

Multiuser design of an architecture for social robots in education: teachers, students, and researchers perspectives.

Frontiers in robotics and AI·2024
Same author

An Integrated Neurorobotics Model of the Cerebellar-Basal Ganglia Circuitry.

International journal of neural systems·2023
Same author

Measuring the impact of nonpharmaceutical interventions on the SARS-CoV-2 pandemic at a city level: An agent-based computational modelling study of the City of Natal.

PLOS global public health·2023
Same author

Deep Q-network for social robotics using emotional social signals.

Frontiers in robotics and AI·2022
Same author

Memory-Based Pruning of Deep Neural Networks for IoT Devices Applied to Flood Detection.

Sensors (Basel, Switzerland)·2021
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关实验视频

Updated: Jul 22, 2026

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.4K

一种基于人类活动识别的神经机器人方法来选择行为.

Caetano M Ranieri1, Renan C Moioli2, Patricia A Vargas3

  • 1Institute of Mathematical and Computer Sciences, University of Sao Paulo, Avenida Trabalhador Sao Carlense, 400, Sao Carlos, SP 13566-590 Brazil.

Cognitive neurodynamics
|July 31, 2023
PubMed
概括

这项研究引入了一个神经机器人模型,让机器人在人机交互期间有效地选择行为. 该模型将大脑电路模拟与人类活动识别相结合,改进了自主机器人的反应.

关键词:
行为选择行为选择.生物启发的计算模型人类活动识别 人类活动识别神经机器人学是一种神经机器人学.机器人模拟机器人模拟器

更多相关视频

SSVEP-based Experimental Procedure for Brain-Robot Interaction with Humanoid Robots
11:01

SSVEP-based Experimental Procedure for Brain-Robot Interaction with Humanoid Robots

Published on: November 24, 2015

13.2K
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

3.8K

相关实验视频

Last Updated: Jul 22, 2026

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.4K
SSVEP-based Experimental Procedure for Brain-Robot Interaction with Humanoid Robots
11:01

SSVEP-based Experimental Procedure for Brain-Robot Interaction with Humanoid Robots

Published on: November 24, 2015

13.2K
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

3.8K

科学领域:

  • 机器人技术 机器人技术 机器人技术
  • 神经科学是一个神经科学.
  • 人与机器人的交互

背景情况:

  • 有效的人机交互依赖于强大的人类活动识别和机器人行为选择.
  • 当前的方法经常使用确定性链接,忽视实时预测不确定性.
  • 自主机器人需要复杂的决策能力,以实现无互动.

研究的目的:

  • 为机器人行为选择提供初始的神经机器人模型.
  • 整合基底 - thalamus - 皮质 (BG-T-C) 电路的计算模型与人类活动识别.
  • 在人机交互中解决决定性行为选择的局限性.

主要方法:

  • 开发了一个模拟哺乳动物大脑电路 (BG-T-C) 的神经机器人模型.
  • 将模型与先进的人类活动识别技术相结合.
  • 利用机器人模拟环境与智能家居环境中的移动机器人.

主要成果:

  • 神经机器人模型展示了有利的行为选择能力.
  • 该模型有效地将人类活动识别与机器人行为联系起来.
  • 通过更准确的活动识别和复杂的动物模型,性能得到了提高.

结论:

  • 拟议的神经机器人模型为自适应机器人行为提供了一个有希望的方法.
  • 整合神经生理模型可以在以人为中心的环境中增强机器人的自主性.
  • 这项工作突出了生物灵感人工智能的潜力,用于先进的机器人.