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相关概念视频

Reinforcement01:23

Reinforcement

266
Positive and negative reinforcement are key concepts in operant conditioning, a learning process where the consequences of a behavior affect the likelihood of that behavior being repeated.
Positive reinforcement occurs when a behavior is followed by the presentation of a rewarding stimulus, increasing the frequency of that behavior. For example:
266
Instinctive Drift01:05

Instinctive Drift

242
Instinctive drift refers to the tendency of animals to revert to their innate behaviors despite repeated reinforcement. Breland and Breland demonstrated this concept in an experiment with a raccoon. The raccoon was trained to pick up two coins and place them in a container in exchange for food. Initially, the raccoon learned to associate the coins with food, making them a conditioned stimulus or a substitute for food. However, over time, the raccoon became less willing to put the coins into the...
242
Reinforcement Schedules01:24

Reinforcement Schedules

197
Positive reinforcement is a powerful method for teaching new behaviors to both animals and humans. B.F. Skinner demonstrated this with his experiments using rats in a Skinner box. When a rat pressed a lever, it received a food pellet. This immediate reward encouraged the rat to repeat the behavior. This method, where a reward follows every instance of the behavior, is known as continuous reinforcement. It is highly effective for establishing new behaviors quickly.
Once a behavior is learned,...
197
Purposive Learning01:22

Purposive Learning

135
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...
135
Law of Effect01:06

Law of Effect

1.4K
B.F. Skinner, a prominent figure in behavioral psychology, introduced operant conditioning by emphasizing the role of consequences in shaping behavior. This theory builds upon the law of effect proposed by Edward Thorndike, which posits that behaviors followed by satisfying outcomes are likely to be repeated. In contrast, those followed by unsatisfying outcomes are less likely to recur.
Edward Thorndike's foundational work involved studying learning in animals, particularly using puzzle...
1.4K
Naturalistic Observations02:30

Naturalistic Observations

15.5K
If you want to understand how behavior occurs, one of the best ways to gain information is to simply observe the behavior in its natural context. However, people might change their behavior in unexpected ways if they know they are being observed. How do researchers obtain accurate information when people tend to hide their natural behavior? As an example, imagine that your professor asks everyone in your class to raise their hand if they always wash their hands after using the restroom. Chances...
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相关实验视频

Updated: Jul 15, 2025

Pavlovian Conditioned Approach Training in Rats
06:57

Pavlovian Conditioned Approach Training in Rats

Published on: February 4, 2016

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自然主义的强化学习学习.

Toby Wise1, Kara Emery2, Angela Radulescu3

  • 1Department of Neuroimaging, King's College London, London, UK.

Trends in cognitive sciences
|September 30, 2023
PubMed
概括
此摘要是机器生成的。

这篇评论探讨了认知神经科学中的自然主义方法如何在复杂的现实环境中揭示人类的决策. 了解这些自然主义策略提供了超越简化强化学习模型的见解.

关键词:
计算建模计算建模在决策过程中做出决定.这是一个自然主义的自然主义者.强化学习是一种强化学习.

更多相关视频

Recording Single Neurons' Action Potentials from Freely Moving Pigeons Across Three Stages of Learning
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Operant Sensation Seeking in the Mouse
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相关实验视频

Last Updated: Jul 15, 2025

Pavlovian Conditioned Approach Training in Rats
06:57

Pavlovian Conditioned Approach Training in Rats

Published on: February 4, 2016

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Recording Single Neurons' Action Potentials from Freely Moving Pigeons Across Three Stages of Learning
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Recording Single Neurons' Action Potentials from Freely Moving Pigeons Across Three Stages of Learning

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Operant Sensation Seeking in the Mouse
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科学领域:

  • 认知神经科学 认知神经科学
  • 计算神经科学是一种神经科学.
  • 决策科学 决策科学 决策科学

背景情况:

  • 人类的决策是复杂的,发生在广的,多维的现实世界的设置.
  • 传统的认知计算神经科学经常使用简化的人工任务来通过强化学习 (RL) 研究决策.
  • 这种简化可能不能完全捕捉现实世界人类选择的细微差别.

研究的目的:

  • 审查自然主义方法,研究人类在复杂环境中的决策.
  • 为了更清楚地了解人类如何应对现实世界的决策挑战.
  • 识别在复杂,多维环境中成功导航的基础上的认知过程.

主要方法:

  • 对采用自然主义方法的最新研究进行审查.
  • 实验范式包括现实世界复杂性的元素.
  • 专注于复杂的,多维的环境,而不是过度简化.

主要成果:

  • 自然主义的方法提供了一个更准确的视图的人类决策在复杂的环境中.
  • 这些方法揭示了超越传统RL框架的认知过程的洞察力.
  • 研究强调了人类成功地在复杂的环境中导航.

结论:

  • 自然主义研究对于理解现实世界的人类决策至关重要.
  • 超越简化任务,可以更清楚地了解认知策略.
  • 这种方法增强了我们对人类如何应对复杂的多维选择的理解.