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相关概念视频

High-Level and Low-Level Awareness01:19

High-Level and Low-Level Awareness

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Controlled processes in human consciousness represent high-alert mental states where individuals deliberately focus their attention on achieving specific goals. Controlled processes can be seen in situations like mastering new technology, where a person might become so absorbed that they ignore surrounding distractions. Such processes involve selective attention, requiring one to concentrate on particular elements of experience while disregarding others. These are governed by executive...
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Reason and Intuition01:37

Reason and Intuition

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The human brain processes information for decision-making using one of two routes: an intuitive system and a rational system (Epstein, 1994; popularized by Kahneman, 2011 as System 1 and System 2, respectively). The intuitive system is quick, impulsive, and operates with minimal effort, relying on emotions or habits to provide cues for what to do next, while the rational system is logical, analytical, deliberate, and methodical. Research in neuropsychology suggests that the...
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The Availability Heuristic01:08

The Availability Heuristic

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A heuristic is a general problem-solving framework (Tversky & Kahneman, 1974). You can think of these as mental shortcuts that are used to solve problems. Different types of heuristics are used in different types of situations, and the impulse to use a heuristic occurs when one of five conditions is met (Pratkanis, 1989):
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Optimal Arousal Theory01:23

Optimal Arousal Theory

95
The optimal arousal theory suggests that performance is maximized when an individual experiences a moderate level of arousal. This theory is closely tied to the Yerkes-Dodson law, which illustrates an inverted U-shaped relationship between arousal and performance. The law, formulated by psychologists Robert Yerkes and John Dodson, implies an ideal arousal level for optimal performance, and deviations from this level can lead to declines in effectiveness.
Inverted U-Shaped Performance Curve
The...
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Quantifying Work02:30

Quantifying Work

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As a system undergoes a change, its internal energy can change, and energy can be transferred from the system to the surroundings, or from the surroundings to the system. 
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相关实验视频

Updated: May 22, 2025

The Joint Effect of Social Comparison and Social Distance on Evaluation of Intertemporal Choice Outcomes in Event-related Potential Studies
08:24

The Joint Effect of Social Comparison and Social Distance on Evaluation of Intertemporal Choice Outcomes in Event-related Potential Studies

Published on: August 25, 2023

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工作负载和任务优先级对多任务性能的影响和依赖1级可解释AI (XAI) 使用

Jawad Alami1, Mohamad El Iskandarani1, Sara Lu Riggs1

  • 1University of Virginia, USA.

Human factors
|March 12, 2025
PubMed
概括

关键任务中的高工作负载增加了对AI警报的依赖,但减少了警报验证. 任务优先级也会影响人工智能解释的使用,这对于在高风险环境中校准人工智能信任至关重要.

科学领域:

  • 人与计算机的交互
  • 认知心理学 认知心理学
  • 人工智能的人工智能

背景情况:

  • 在关键环境中的运营商面临着影响业绩的多任务挑战.
  • 可解释的人工智能 (XAI) 可以支持决策,但其在多任务处理中的应用尚未得到充分理解.
  • 一级XAI为援助运营商提供基本的感知信息.

研究的目的:

  • 检查工作负载和任务优先级如何影响多任务性能.
  • 在高风险场景中调查运营商对1级XAI系统的依赖.
  • 了解工作负载,任务优先级和XAI利用之间的相互作用.

主要方法:

  • 一个在实验对象内部的实验,与30名参与者进行模拟无人机指挥和控制任务.
  • 操纵工作量 (低,中,高) 和人工智能辅助任务优先级 (低,高).
  • 测量性能指标,包括准确性,人工智能依赖性和警报检测.

主要成果:

  • 增加的工作量降低了人工智能辅助任务的性能,并增加了对人工智能系统的依赖,特别是在任务优先级较低的情况下.
  • 任务优先级显著影响了AI解释的使用.
  • 运营商在工作量高的情况下对人工智能警报的依赖增加,但警报验证减少.
关键词:
自动化自动化自动化自动化解释 解释 解释多任务处理是多任务处理.业绩表现表现的表现表现是什么依赖的依赖依赖的依赖工作负载的工作负载.

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结论:

  • 工作负载影响运营商对人工智能的依赖,需要仔细校准人工智能对关键系统的信任.
  • 任务优先级是操作员如何处理AI解释的一个关键因素.
  • 结果为高风险环境的AI系统的设计提供了信息,以确保适当的AI依赖.