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相关概念视频

Decision Making01:20

Decision Making

250
Decision-making is a fundamental cognitive process that involves evaluating alternatives and selecting among them. This process can range from simple choices, such as deciding what to wear, to complex decisions, like choosing a major in college or a career path. The complexity of the decision often dictates the approach we use, which can be broadly categorized into two types: automatic and controlled decision-making.
Automatic decision-making is fast, intuitive, and relies on gut feelings...
250
Decision Making: Traditional Method01:14

Decision Making: Traditional Method

4.2K
The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
4.2K
Reason and Intuition01:37

Reason and Intuition

6.9K
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...
6.9K
Heuristics01:21

Heuristics

154
Heuristics are problem-solving strategies that use mental shortcuts to simplify decision-making. Unlike algorithms, which must be followed precisely to achieve a correct result, heuristics offer a general problem-solving framework. They save time and energy but can sometimes lead to less rational decisions.
People often rely on heuristics when faced with an overload of information, limited time, low importance of the decision, limited information, or when a heuristic readily comes to mind. For...
154
Decision Making: P-value Method01:09

Decision Making: P-value Method

5.8K
The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can...
5.8K
Manipulation and Analysis01:21

Manipulation and Analysis

70
GIS manipulation and analysis functions are vital for decision-making and planning. These activities range from data retrieval tasks, such as selecting information based on specific criteria, to advanced analytical techniques that address complex spatial problems.One critical GIS analysis method is overlaying, which combines multiple data layers to examine impacts. For example, overlaying a river-dammed lake boundary with road networks can identify affected infrastructure. Another common...
70

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相关实验视频

Updated: Sep 20, 2025

Integrating Visual Psychophysical Assays within a Y-Maze to Isolate the Role that Visual Features Play in Navigational Decisions
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Integrating Visual Psychophysical Assays within a Y-Maze to Isolate the Role that Visual Features Play in Navigational Decisions

Published on: May 2, 2019

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视觉化决策任务的类型学

Camelia D Brumar, Sam Molnar, Gabriel Appleby

    IEEE transactions on visualization and computer graphics
    |May 22, 2025
    PubMed
    概括

    本研究引入了数据可视化中的决策任务的新类型,将其分类为CHOOSE,ACTIVATE和CREATE. 该框架有助于设计更好的决策支持工具,并澄清复杂的决策过程.

    科学领域:

    • 数据可视化 数据可视化
    • 人与计算机的交互
    • 决策科学 决策科学 决策科学

    背景情况:

    • 数据可视化旨在支持决策,但缺乏针对复杂决策的具体任务区分.
    • 现有的分类法经常集中在低级分析任务上,不足以设计决策支持工具.

    研究的目的:

    • 为数据可视化社区介绍一个简洁的决策任务类型 (选择,激活,创建).
    • 为可视化环境提供适合的定义,并使复杂决策的灵活描述成为可能.

    主要方法:

    • 基于文献综述中的设计目标,对决策任务类型学的代改进.
    • 通过四个案例研究和与可视化专家的半结构面试进行验证.

    主要成果:

    • 开发了一个三项任务类型 (CHOOSE,ACTIVATE,CREATE),为复杂的决策提供分层组织.
    • 案例研究和专家采访证明了类型学在分析决策过程中的描述能力和有效性.

    结论:

    • 拟议的类型学增强了可视化设计师和领域专家之间的对话.
    • 它提供了一个灵活的框架来描述和设计数据可视化中的决策支持系统.

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