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相关概念视频

The Uncertainty Principle04:08

The Uncertainty Principle

31.9K
Werner Heisenberg considered the limits of how accurately one can measure properties of an electron or other microscopic particles. He determined that there is a fundamental limit to how accurately one can measure both a particle’s position and its momentum simultaneously. The more accurate the measurement of the momentum of a particle is known, the less accurate the position at that time is known and vice versa. This is what is now called the Heisenberg uncertainty principle. He...
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Uncertainty in Measurement: Reading Instruments02:46

Uncertainty in Measurement: Reading Instruments

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Counting is the type of measurement that is free from uncertainty, provided the number of objects being counted does not change during the process. Such measurements result in exact numbers. By counting the eggs in a carton, for instance, one can determine exactly how many eggs are there in the carton. Similarly, the numbers of defined quantities are also exact. For example, 1 foot is exactly 12 inches, 1 inch is exactly 2.54 centimeters, and 1 gram is exactly 0.001 kilograms. Quantities...
52.0K
Uncertainty: Overview00:59

Uncertainty: Overview

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In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
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Uncertainty in Measurement: Significant Figures03:34

Uncertainty in Measurement: Significant Figures

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All the digits in a measurement, including the uncertain last digit, are called significant figures or significant digits. Note that zero may be a measured value; for example, if a scale that shows weight to the nearest pound reads “140,” then the 1 (hundreds), 4 (tens), and 0 (ones) are all significant (measured) values.
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Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

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The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...
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Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

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An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
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相关实验视频

Updated: Feb 4, 2026

Using Practice Testing, Public Speaking, and Source Monitoring to Examine the Influences of Learning Strategies and Stress on Episodic Memory
07:59

Using Practice Testing, Public Speaking, and Source Monitoring to Examine the Influences of Learning Strategies and Stress on Episodic Memory

Published on: June 14, 2019

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信息不确定性会影响学习策略,从顺序延迟的奖励开始.

Sean R Maulhardt1, Alec Solway1, Caroline J Charpentier1,2

  • 1Department of Psychology, University of Maryland College Park, College Park, Maryland, United States of America.

PLoS computational biology
|February 2, 2026
PubMed
概括

人类通过选择两种策略来解决时间信用分配:资格跟踪和表格更新. 减少不确定性有利于表式策略,提高奖励学习准确性.

科学领域:

  • 认知科学 认知科学
  • 计算神经科学是一种神经科学.
  • 行为经济学是一种行为经济学.

背景情况:

  • 时间信用分配,确定哪个过去事件导致了奖励,由于环境不确定性而具有挑战性.
  • 现有的研究往往孤立了延迟和奖励维度,使算法解决方案和不确定性效应未得到充分探索.

研究的目的:

  • 在不同程度的信息不确定性下,研究人类的时间信用分配策略.
  • 为了比较两个计算模型的有效性:资格跟踪和表格更新.

主要方法:

  • 调整了一个奖励学习任务,延迟奖励,中间事件和操纵信息不确定性.
  • 开发并比较了两个计算学习模型:资格跟踪和表格更新.
  • 对人类参与者行为进行验证的模型预测 (N=142).

主要成果:

  • 两种模型都学习了任务,并预测了参与者选择和信用分配签名.
  • 在信息不确定性低的情况下,表格模型显著超过了资格跟踪模型.
  • 减少不确定性与使用表式策略的增加以及较高的表式权重相关.

结论:

  • 人类时间信用分配策略根据环境信息的不确定性进行调整.

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  • 当不确定性较低时,表格更新策略比资格跟踪更有效.
  • 研究结果提供了关于在动态环境中适应性学习机制的见解.