相关概念视频
01:12Hindsight Biases
3.4K
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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01:15Expected Value
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The expected value is known as the "long-term" average or mean. This means that over the long term of experimenting over and over, you would expect this average. The expected average is represented by the symbol μ. It is calculated as follows:
3.9K
01:09Decision Making: P-value Method
5.3K
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...
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...
5.3K
01:31Confirmation Biases
5.5K
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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01:03Prediction Intervals
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The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
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02:09Stereotype Threat and Self-fulfilling Prophecies
37.6K
When we hold a stereotype about a person, we have expectations that he or she will fulfill that stereotype. A self-fulfilling prophecy is an expectation held by a person that alters his or her behavior in a way that tends to make it true. When we hold stereotypes about a person, we tend to treat the person according to our expectations. This treatment can influence the person to act according to our stereotypic expectations, thus confirming our stereotypic beliefs. Research by Rosenthal and...
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隐藏的奖励:影响及其预测错误作为进入主观价值的窗口
Marius C Vollberg1,2,3, David Sander2,3
1Department of Psychology, University of Amsterdam.
Current directions in psychological science
|April 2, 2024
概括
强化学习概念,如预测错误,可以解释主观的感受和价值. 研究人员应该测试这些概念影响研究,而不是假设它们直接适用.
科学领域:
- 认知科学 认知科学
- 神经科学是一个神经科学.
- 心理学 心理学 心理学
背景情况:
- 强化学习 (RL) 概念越来越多地应用于影响的研究.
- 奖励预测错误是RL研究适应性行为的基础.
- 从历史上看,RL研究专注于可观察到的奖励,对主观情感的关注较少.
研究的目的:
- 探索RL概念的应用,以影响.
- 调查基于情感的预测错误的解释力.
- 为阐明主观价值提出RL概念.
主要方法:
- 审查和概念分析强化学习的原则,应用于影响.
- 对人类和动物奖励预测错误的现有研究进行了审查.
- 讨论心理研究中对情感主义的转变.
主要成果:
- 情感预测错误 (预期和经历的情感之间的差异) 提供了超出可观测奖励的解释能力.
- 主观价值可以通过将RL概念应用于影响来阐明.
- 人类报告预期和经历影响的能力是关键因素.
结论:
- 将RL概念应用于影响,显示出理解主观价值的前景.
- 科学家应该批判性地测试RL概念,以确定它们对影响的适用性.
- 未来的研究应该专注于情感预测错误的独特方面.