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相关概念视频

Observational Learning01:12

Observational Learning

132
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
132
Steps in the Modeling Process01:14

Steps in the Modeling Process

177
Albert Bandura's theory of observational learning identifies four critical processes: attention, retention, motor reproduction, and reinforcement or motivation.
Attention is the first necessary component for observational learning. It involves focusing on what the model is doing and saying. For example, if you decide to take a drawing class to enhance your skills, you need to pay close attention to the instructor's words and hand movements. The characteristics of the model significantly...
177
Modeling in Therapy01:26

Modeling in Therapy

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Modeling, a key technique in therapy, uses observational learning to help clients acquire and practice new skills by watching therapists demonstrate desired behaviors. This approach, rooted in Albert Bandura's concept of vicarious learning, plays a significant role in therapeutic interventions for various psychological conditions, including social anxiety, ADHD, and depression.
Participant Modeling
Participant modeling involves therapists demonstrating calm and effective behaviors in...
48
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

40
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
40
Hindsight Biases01:12

Hindsight 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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Cognitive Learning01:21

Cognitive Learning

220
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...
220

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相关实验视频

Updated: Jun 5, 2025

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
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从缺失的反中学习:示例与基于模型的方法.

Jerker Denrell1, Adam N Sanborn2, Jake Spicer2

  • 1Warwick Business School, University of Warwick.

Journal of experimental psychology. Learning, memory, and cognition
|December 12, 2024
PubMed
概括
此摘要是机器生成的。

人们通过使用示例或基于模型的学习策略来适应偏见的反. 有些人认为缺失的负面结果,而另一些人则使用贝叶斯模型来纠正选择偏差.

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科学领域:

  • 认知心理学 认知心理学
  • 机器学习是机器学习.
  • 决策科学科学 决策科学

背景情况:

  • 现实世界的反往往是有选择的,导致偏见的样本有利于积极的结果.
  • 决策者面临的挑战是从这种有偏见的数据中学习,并纠正固有的偏见.

研究的目的:

  • 研究个人如何从选择性偏见的反中学习.
  • 为了确定人们是否可以纠正学习中的选择偏差.
  • 为了比较示例和基于模型的学习方法来处理缺失的反.

主要方法:

  • 描述了从偏向样本中学习分类的计算问题.
  • 检查了示例模型 (归因缺失的负面结果) 和基于模型的方法 (调整任务表示).
  • 进行了三项实验,以测试依赖归算与贝叶斯模型对偏差校正的依赖性.

主要成果:

  • 参与者采用了多种不同的策略,其中许多最好用示例模型 (通常与归算) 来描述.
  • 几乎相同比例的参与者最好用贝叶斯模型来描述.
  • 个别策略在任务之间显示了一些稳定性,但受到任务不确定性的影响.

结论:

  • 人们通过各种方法来适应处理缺失的反.
  • 学习者使用基于示例的归算和基于模型的贝叶斯校正.
  • 策略选择受到任务假设和背景的影响,证明了自适应性学习.