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相关概念视频

Weighted Mean00:57

Weighted Mean

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While taking the arithmetic, geometric, or harmonic mean of a sample data set, equal importance is assigned to all the data points. However, all the values may not always be equally important in some data sets. An intrinsic bias might make it more important to give more weightage to specific values over others.
For example, consider the number of goals scored in the matches of a tournament. While computing the average number of goals scored in the tournament, it may be more important to...
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Bias01:22

Bias

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Bias refers to any tendency that prevents a question from being considered unprejudiced. In research, bias occurs when one outcome or answer is selected or encouraged over others in sampling or testing. Bias can occur during any research phase, including study design, data collection, analysis, and publication.
In statistics, a sampling bias is created when a sample is collected from a population, and some members of the population are not as likely to be chosen as others (remember, each member...
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Hindsight Biases01:12

Hindsight Biases

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Hindsight bias leads you to believe that the event you just experienced was predictable, even though it really wasn’t. In other words, you knew all along that things would turn out the way they did. Can you relate this to the phrase "Hindsight is 20/20" now? 
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Confirmation Biases01:31

Confirmation Biases

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The confirmation bias is the tendency to focus on information that confirms our existing beliefs and ignore information that is inconsistent with our expectations. For example, if you think that your professor is not very nice, you notice all of the instances of rude behavior exhibited by the professor while ignoring the countless pleasant interactions he is involved in on a daily basis. Have you ever fallen prey to the confirmation bias, either as the source or target of such bias?
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The Anchoring-and-Adjustment Heuristic01:25

The Anchoring-and-Adjustment Heuristic

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In order to make good decisions, we use our knowledge and our reasoning. Often, this knowledge and reasoning is sound and solid. However, sometimes, we are swayed by biases or by others manipulating a situation. For example, let’s say you and three friends wanted to rent a house and had a combined target budget of $1,600. The realtor shows you only very run-down houses for $1,600 and then shows you a very nice house for $2,000. Might you ask each person to pay more in rent to get the...
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Fundamental Attribution Error01:14

Fundamental Attribution Error

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According to some social psychologists, people tend to overemphasize internal factors as explanations—or attributions—for the behavior of other people. They tend to assume that the behavior of another person is a trait of that person, and to underestimate the power of the situation on the behavior of others. They tend to fail to recognize when the behavior of another is due to situational variables, and thus to the person’s state. This erroneous assumption is...
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相关实验视频

Updated: Sep 10, 2025

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
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A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

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认知偏差作为贝叶斯概率权重

Bruno Kopp1

  • 1Cognitive Neuropsychology, Department of Neurology, Hannover Medical School, Hannover, Germany.

Frontiers in psychology
|August 22, 2025
PubMed
概括

认知偏见如保守主义和基准率忽视取决于背景. 适应贝叶斯认知 (ABC) 模型将这些偏见解释为不确定性下决策的适应性策略,而不是错误.

科学领域:

  • 认知心理学
  • 行为经济学
  • 计算模型

背景情况:

  • 人类在不确定性下的判断往往表现出系统的偏见,包括保守主义偏见和基准率忽视.
  • 了解这些偏见的上下文依赖性对于认知和经济理论至关重要.

研究的目的:

  • 研究任务环境如何影响人类概率判断的系统偏见.
  • 探索先前概率和证据可能性在不确定性下决策中的作用.
  • 提出并验证解释这些上下文依赖偏见的计算模型.

主要方法:

  • 48名参与者完成了12个需要主观概率判断的场景.
  • 场景包括在不同背景下整合先前的概率与证据的概率 (小世界与大世界).
  • 概率信息以概率和相对频率的形式呈现.

主要成果:

  • 任务上下文对先验和证据的权重有很大影响.
  • 小世界场景通过强调先验而放大了保守主义偏见.
  • 大世界场景增加了对证据的敏感性,导致基准率被忽视;相对频率没有减轻偏见.

结论:

  • 适应贝叶斯认知 (ABC) 模型将认知偏见解释为受上下文,能力限制和元学习影响的适应性策略.
关键词:
贝叶斯推理认知偏见计算模型进行决策决策支持从背景中学习

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  • 这些研究结合了认知心理学,行为经济学和计算建模,
  • 这项研究通过突出认知偏差的适应性来告知决策支持系统的设计.