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相关概念视频

Cognitive Learning01:21

Cognitive Learning

997
Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
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Purposive Learning01:22

Purposive Learning

435
E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
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Decision Making: P-value Method01:09

Decision Making: P-value Method

6.8K
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...
6.8K
Reason and Intuition01:37

Reason and Intuition

7.4K
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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Framing Effects03:26

Framing Effects

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Information is everywhere and its presentation—such as how and when items are presented—can impact our perceptions and decisions surrounding the info. This broad concept umbrellas framing effects—influences that occur due to the way information is framed in its appearance, whether it’s purely the order or the specific wording of a message. Let’s take a look at numerous ways in which two versions of something can objectively say the same thing, yet we respond in...
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Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

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Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
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相关实验视频

Updated: Jan 13, 2026

Experimental Research Examining How People Can Cope with Uncertainty Through Soft Haptic Sensations
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一个信息理论框架,用于理解不确定性下的学习和选择.

Jae Hyung Woo1, Lakshana Balaji2, Alireza Soltani1

  • 1Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH 03755, USA.

Entropy (Basel, Switzerland)
|October 28, 2025
PubMed
概括

信息理论为分析行为数据的决策和学习策略提供了一个新的框架. 这种方法揭示了偏见,元可塑性和选择调整,为传统方法提供了无参数的替代方案.

关键词:
有条件的.这是相互信息的互惠.强化学习是一种强化学习.不确定性是一种不确定性.以价值为基础的决策.

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科学领域:

  • 计算神经科学是一种计算神经科学.
  • 行为经济学是一种行为经济学.
  • 信息理论是信息理论.

背景情况:

  • 信息理论在神经科学中广泛用于神经活动分析.
  • 它对行为数据的应用,特别是像选择和奖励这样的离散变量,研究较少.
  • 离散的行为数据非常适合信息理论分析.

研究的目的:

  • 用信息理论为分析不确定性下决策和学习策略提供一个框架.
  • 为了证明行为指标如何推断潜在的认知机制.
  • 突出信息理论作为一个多功能,参数-免费的工具用于认知任务.

主要方法:

  • 利用模拟强化学习模型作为基础真理.
  • 应用信息理论指标,包括条件和相互信息.
  • 分析了离散的,逐个试验的行为数据.

主要成果:

  • 确定了学习率的积极性偏差 (奖励更高).
  • 检测到学习率的历史依赖的变化,表明了转塑性.
  • 揭示了选择策略和替代学习策略的奖励收获率驱动的调整.

结论:

  • 信息理论为分析复杂的行为策略提供了一个强大的,无参数的框架.
  • 这种方法可以揭示在不确定性下学习和决策的细微方面.
  • 该框架有可能在认知科学和神经科学中得到更广泛的应用.