Jove
Visualize
联系我们
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

相关概念视频

Confidence Coefficient01:24

Confidence Coefficient

10.5K
The confidence coefficient is also known as the confidence level or degree of confidence. It is the percent expression for the probability, 1-α, that the confidence interval contains the true population parameter assuming that the confidence interval is obtained after sufficient unbiased sampling; for example, if the CL = 90%, then in 90 out of 100 samples the interval estimate will enclose the true population parameter. Here α is the area under the curve, distributed equally under...
10.5K
Measures of Intelligence01:29

Measures of Intelligence

8.3K
Psychologists measure intelligence by using standardized tests that produce a score known as the intelligence quotient or IQ. To understand IQ tests, it's important to recognize the key principles behind their construction: validity, reliability, and standardization.
Validity refers to how well a test measures what it claims to measure. An intelligence test should accurately assess intelligence rather than another characteristic, like anxiety. Criterion validity is one way to evaluate this;...
8.3K
Binet's Contribution to Measures of Intelligence01:23

Binet's Contribution to Measures of Intelligence

1.7K
Alfred Binet, along with his student Théophile Simon, was tasked by the French Ministry of Education in 1904 to create a method for identifying students who struggled to learn through conventional classroom instruction. This initiative aimed to address overcrowding by placing such students in specialized schools. Binet and Simon developed an intelligence test comprising 30 tasks, ranging from simple commands, like touching one's nose or ear, to more complex tasks, such as drawing...
1.7K
Reliability and Validity01:29

Reliability and Validity

13.7K
Reliability and validity are two important considerations that must be made with any type of data collection. Reliability refers to the ability to consistently produce a given result. In the context of psychological research, this would mean that any instruments or tools used to collect data do so in consistent, reproducible ways.
13.7K
Triarchic Theory of Intelligence01:24

Triarchic Theory of Intelligence

9.8K
Robert Sternberg's triarchic theory of intelligence posits that intelligence is composed of three distinct but interrelated components: analytical, creative, and practical intelligence.
9.8K
Self-Report Tests of Personality01:22

Self-Report Tests of Personality

776
Self-report inventories are objective personality assessments that use multiple-choice items or numbered scales, typically ranging from 1 (strongly disagree) to 5 (strongly agree). They are often called Likert scales after Rensis Likert. These inventories are widely used due to their ease of administration and cost-effectiveness. One of the most prominent examples is the Minnesota Multiphasic Personality Inventory (MMPI), initially developed in the 1940s to assess abnormal personality traits.
776

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

Humans do not "play fair" with social robots: Unequal task sharing in an attentional task.

Acta psychologica·2026
Same author

Humans take the visuospatial perspective of robots and objects that imply social presence.

Acta psychologica·2025
Same author

Reward-modulated attention deployment is driven by suppression, not attentional capture.

NeuroImage·2024
Same author

Social perception of robots is shaped by beliefs about their minds.

Scientific reports·2024
Same author

Specialty Grand Challenge Article- Social Neuroergonomics.

Frontiers in neuroergonomics·2024
Same author

Robots engage face-processing less strongly than humans.

Frontiers in neuroergonomics·2024
Same journal

Lifespan Trajectories of the Brain's Functional Complexity Characterized by Multiscale Sample Entropy.

NeuroImage·2026
Same journal

Pleasant fragrance modulates dyadic social sharing of positive emotion: Sharer-centered socioemotional enhancement effect and its neural couplings.

NeuroImage·2026
Same journal

Altered Functional Hierarchical and Sequential Organization in Individuals with Schizophrenia during Auditory Processing.

NeuroImage·2026
Same journal

Mechanical Deformation Explains Distinct Neuroimaging Patterns and Etiologies in Brain Trauma.

NeuroImage·2026
Same journal

Ventral striatum temporal interference brain stimulation enhances the reward-positivity event-related potential and reduces anxiety.

NeuroImage·2026
Same journal

NeuroHarm‑Kit: An Open‑Source Toolbox for Benchmarking Deep‑Learning Harmonization of Multi‑Site T1‑Weighted MRI.

NeuroImage·2026
查看所有相关文章

相关实验视频

Updated: Jan 18, 2026

Characterization of the Sense of Agency over the Actions of Neural-machine Interface-operated Prostheses
05:21

Characterization of the Sense of Agency over the Actions of Neural-machine Interface-operated Prostheses

Published on: January 7, 2019

8.3K

用N2pc组件测量对人工智能的信任

Eva Wiese1, Tobias Feldmann-Wüstefeld2

  • 1Institute of Psychology and Ergonomics, Berlin Institute of Technology, Berlin, Germany; Human Factors and Applied Cognition, George Mason University, Fairfax, USA.

NeuroImage
|January 15, 2026
PubMed
概括
此摘要是机器生成的。

人类和AI的合作需要有效的关注. 一种新的EEG方法跟踪注意力共享,显示像N2pc这样的神经标记在视觉搜索任务中反映了对AI能力的信任.

关键词:
XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX XX X X X X X X X X X X X XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX

更多相关视频

The Collective Trust Game: An Online Group Adaptation of the Trust Game Based on the HoneyComb Paradigm
06:18

The Collective Trust Game: An Online Group Adaptation of the Trust Game Based on the HoneyComb Paradigm

Published on: October 20, 2022

2.5K
Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.5K

相关实验视频

Last Updated: Jan 18, 2026

Characterization of the Sense of Agency over the Actions of Neural-machine Interface-operated Prostheses
05:21

Characterization of the Sense of Agency over the Actions of Neural-machine Interface-operated Prostheses

Published on: January 7, 2019

8.3K
The Collective Trust Game: An Online Group Adaptation of the Trust Game Based on the HoneyComb Paradigm
06:18

The Collective Trust Game: An Online Group Adaptation of the Trust Game Based on the HoneyComb Paradigm

Published on: October 20, 2022

2.5K
Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.5K

科学领域:

  • 认知科学 认知科学
  • 神经科学是一个神经科学.
  • 人与计算机的交互

背景情况:

  • 有效的注意力分配对于人类-人工智能协作至关重要,用户必须监控人工智能性能以防止错误.
  • 过度依赖人工智能或过度监控人工智能可能导致性能下降和关键故障.
  • 对人工智能的信任是影响注意力的关键因素,但很难直接衡量.

研究的目的:

  • 引入和验证基于脑电图 (EEG) 的方法,用于直接跟踪人类和人工智能之间的注意力资源共享.
  • 调查人工智能能力如何影响人类注意力部署和合作任务期间的信任校准.
  • 建立神经生理学标记作为对人工智能系统信任的隐性衡量标准.

主要方法:

  • 参与者从事视觉搜索任务,与不同能力水平的AI合作.
  • 使用脑电图 (EEG) 来记录大脑活动.
  • 测量了选择性视觉注意力的神经标记物N2pc成分,以量化注意力部署.

主要成果:

  • N2pc的振幅被人工智能的能力显著调节.
  • 较小的N2pc振幅与与高能力AI相比较的低能力AI进行交互时增加的注意力卸载和信任相关.
  • 这些发现表明,神经标记可以隐含地反映人类-人工智能合作中的信任校准.

结论:

  • 该N2pc组件作为一个有效和非破坏性的神经生理学标记,用于量化人类-AI协作搜索任务中的注意分配.
  • 这种基于EEG的方法为隐式测量对人工智能的信任提供了一个有前途的方法,进步了我们对信任校准的理解.
  • 该研究将N2pc的应用从视觉注意力研究扩展到对自动化的信任这一关键领域.